Forest fires represent one of the most devastating natural disasters, causing irreversible damage to ecological systems, destroying vegetation, threatening wildlife habitats, and endangering human lives and property. The ability to detect forest fires at their earliest stage is critical for minimizing damage and enabling rapid response. Traditional methods of forest fire monitoring, such as manual patrols and watchtower observation, suffer from low efficiency, high cost, and limited coverage, making them unsuitable for large-scale applications. With the rapid advancement of deep learning and computer vision technologies, object detection algorithms based on convolutional neural networks have achieved remarkable success in various domains, including forest fire detection.
In this study, we propose an improved YOLOv11n-based method for early forest fire detection specifically designed for deployment on China drone platforms. The motivation behind this work stems from the unique challenges associated with detecting forest fires from aerial perspectives: the targets (flames and smoke) are often small in size, exhibit weak texture features, and are embedded in complex background environments. These challenges are particularly pronounced during the early stages of a fire, when both flames and smoke are subtle and easily obscured by vegetation, clouds, or atmospheric conditions. To address these issues, we introduce three key improvements to the baseline YOLOv11n model: a lightweight Adown downsampling module, a hybrid aggregation network (MANet), and a Separated and Enhancement Attention Module (SEAM) detection head. Our experimental results demonstrate significant improvements in detection accuracy, recall, and mean average precision while maintaining computational efficiency suitable for real-time deployment on China drone platforms.
The integration of China drone technology with advanced deep learning models opens new possibilities for large-scale, real-time forest fire surveillance. China drone manufacturers have made significant strides in developing UAV platforms capable of long-endurance flights, high-resolution imaging, and edge computing. By deploying our improved detection model on these platforms, we can achieve timely and accurate detection of forest fires, thereby reducing the risk of catastrophic losses. Throughout this paper, we emphasize the role of China drone technology in enabling practical and scalable solutions for forest fire monitoring.
Our contributions can be summarized as follows: First, we introduce the Adown lightweight downsampling module to reduce model parameters and computational complexity while preserving critical feature information. Second, we design the MANet hybrid aggregation network that integrates three distinct convolutional structures to enhance feature extraction capabilities, enabling better representation of small targets and weak textures. Third, we incorporate the SEAM attention mechanism into the detection head to improve detection accuracy under complex background conditions and occlusion scenarios. Finally, we conduct extensive experiments on a carefully curated dataset of early-stage forest fire images, demonstrating the superiority of our approach over existing state-of-the-art methods. The proposed method achieves 84.2% precision, 75.3% recall, and 80.9% mAP@0.5, representing improvements of 2.7, 5.8, and 5.6 percentage points respectively over the baseline YOLOv11n model, while maintaining a parameter count of 3.2 million and computational cost of 6.8 GFLOPs.
The remainder of this paper is organized as follows. Section 2 reviews related work in forest fire detection and object detection algorithms. Section 3 details the architecture of our improved YOLOv11n model, including the Adown module, MANet module, and SEAM detection head. Section 4 presents the experimental setup, dataset, evaluation metrics, and results, including ablation studies and comparative analyses. Section 5 discusses the implications of our findings and outlines directions for future research. Section 6 concludes the paper.
Related Work
Forest fire detection has been an active area of research for decades, with approaches ranging from traditional sensor-based methods to modern deep learning techniques. In recent years, the advent of China drone technology has provided a flexible and cost-effective platform for aerial surveillance, enabling wide-area coverage and rapid deployment. This section reviews existing work in forest fire detection, with a focus on deep learning-based object detection methods and their application to UAV platforms.
Traditional methods for forest fire detection rely on ground-based sensors, satellite imagery, and manual observation. Ground-based sensors, such as smoke detectors and temperature sensors, provide localized monitoring but are limited in coverage and require extensive infrastructure. Satellite-based monitoring offers wide-area coverage but suffers from low temporal resolution and cloud interference, making it unsuitable for real-time detection. Manual patrols, while flexible, are labor-intensive and inefficient for large forested areas. These limitations highlight the need for automated, real-time detection systems that can operate over large areas with high reliability.
With the emergence of deep learning, convolutional neural network-based object detection methods have become the dominant approach for visual recognition tasks. The R-CNN family of models, including Fast R-CNN and Faster R-CNN, introduced region proposal networks for object detection, achieving high accuracy at the cost of computational efficiency. The SSD (Single Shot MultiBox Detector) model improved inference speed by eliminating the region proposal stage, enabling real-time detection on GPU hardware. The YOLO (You Only Look Once) family of models further advanced the state of the art by formulating object detection as a single regression problem, achieving an optimal balance between speed and accuracy. YOLOv11, the latest iteration in this series, introduces several architectural improvements, including the C3K2 feature extraction module, C2PSA attention mechanism, and depthwise separable convolutions in the detection head, resulting in enhanced performance and efficiency.
Several studies have applied deep learning methods to forest fire detection. For instance, a feature-enhanced Faster R-CNN algorithm was proposed for smoke detection in photovoltaic power stations, demonstrating the effectiveness of region-based methods for fire-related detection tasks. Another study optimized the SSD architecture specifically for forest fire characteristics, improving flame feature extraction. The SSD MobileNet model was adapted for flame region labeling in complex environments, reducing false positives and false negatives. More recently, the LEM-YOLO model, based on YOLOv8n, introduced a lightweight multi-scale detection framework that balances accuracy and computational cost. The YOLO-VRG algorithm, an improved version of YOLOv5s, addressed the challenges of complex backgrounds and weak texture features in early forest fire detection. The YOLO-SCW model focused specifically on smoke detection, achieving accurate localization and recognition of forest fire smoke. A semantic segmentation network called Mobile AttentionNet was also proposed for rapid forest fire region detection.
Despite these advances, existing methods still face challenges in detecting early-stage forest fires, particularly when flames and smoke are small, faint, or partially obscured. The detection accuracy of smoke, in particular, is highly susceptible to weather conditions and atmospheric interference. Furthermore, many existing models are designed for ground-based cameras or satellite imagery and do not account for the unique characteristics of aerial imagery captured from China drone platforms. These challenges motivate our work in developing an improved detection model tailored for early forest fire detection from UAV perspectives.
The use of China drone platforms for forest fire monitoring offers several distinct advantages. China drones can be rapidly deployed to remote areas, operate at low altitudes for high-resolution imaging, and carry various sensors for multi-modal data collection. The integration of edge computing capabilities on China drone platforms enables real-time inference without reliance on cloud connectivity, which is critical for timely fire detection in remote forested regions. By optimizing our detection model for deployment on China drone platforms, we aim to provide a practical and effective solution for early forest fire detection.
Methodology
In this section, we present the architecture of our improved YOLOv11n model for early forest fire detection. The baseline YOLOv11 model consists of three main components: a backbone network for feature extraction, a neck network for multi-scale feature fusion, and a decoupled head for object classification and localization. YOLOv11 is available in five variants: YOLOv11n, YOLOv11s, YOLOv11m, YOLOv11l, and YOLOv11x, differing in model depth, width, and computational complexity. Given the constraints of deployment on China drone platforms, we select the smallest variant, YOLOv11n, as our baseline and introduce three key improvements: the Adown lightweight downsampling module, the MANet hybrid aggregation network, and the SEAM attention detection head.
Adown Lightweight Downsampling Module
The first improvement involves replacing the standard convolutional downsampling layers in the backbone network with the lightweight Adown module. In standard convolutional downsampling, a stride greater than one is used to reduce the spatial dimensions of feature maps. However, this approach compresses flame and smoke features to low resolution in early layers, making it difficult for subsequent layers to recover fine details. Additionally, standard convolutions contain a large number of parameters, leading to high computational cost. The Adown module addresses these issues by employing average pooling to reduce spatial dimensions while preserving key information, followed by parallel paths for feature transformation.
The Adown module structure can be described mathematically as follows. Given an input feature map $$X \in \mathbb{R}^{B \times C \times H \times W}$$, the module first applies average pooling to reduce the spatial dimensions by half. The pooled feature map is then split into two branches along the channel dimension:
$$X_1, X_2 = \text{Split}\big(\text{AvgPool2d}(X)\big), \quad X \in \mathbb{R}^{B \times C \times H \times W}$$
In the first branch, we apply max pooling followed by a 1×1 convolution:
$$Y_1 = \text{Conv}_{1 \times 1}\big(\text{MaxPool2d}(X_1)\big)$$
In the second branch, we apply a 3×3 convolution directly:
$$Y_2 = \text{Conv}_{3 \times 3}(X_2)$$
The outputs of the two branches are then concatenated along the channel dimension to produce the final output:
$$Y = \text{Concat}(Y_1, Y_2), \quad Y \in \mathbb{R}^{B \times 2C \times \frac{H}{2} \times \frac{W}{2}}$$
This design reduces the number of parameters and computational cost while maintaining the representational capacity needed for accurate detection. The Adown module achieves this by using average pooling to reduce spatial redundancy, followed by efficient convolutions that operate on lower-resolution feature maps. The split-and-concatenate strategy ensures that information from both branches is preserved, enabling the model to capture both fine details and high-level semantic features.
In our implementation, we replace all standard convolutional downsampling layers in the backbone network with Adown modules. This modification reduces the parameter count of the backbone from 1.8 million to 1.3 million and the computational cost from 4.2 GFLOPs to 3.1 GFLOPs, while maintaining comparable detection accuracy. The Adown module is particularly effective for China drone deployment scenarios where computational resources are limited and real-time performance is essential.
MANet Hybrid Aggregation Network
The second improvement is the design of the MANet (Mixed Aggregation Network) module, which enhances feature extraction by integrating three distinct convolutional structures. Traditional YOLO backbone networks rely on single-type convolutional structures, such as standard convolutions with residual connections, which limit information flow and semantic integration. The MANet module addresses this limitation by combining multiple convolutional pathways to achieve richer information flow and deeper semantic integration, all while maintaining computational efficiency suitable for China drone platforms.
The MANet module architecture follows an “input expansion, multi-path feature extraction, and fusion compression” design. Given an input feature map $$X_{\text{in}}$$, the module first applies a 1×1 convolution to expand the channel dimension to $$2C$$, producing an intermediate feature map $$X_{\text{mid}}$$ that provides sufficient channel capacity for subsequent multi-path processing:
$$X_{\text{mid}} = \text{Conv}_1(X_{\text{in}})$$
Next, $$X_{\text{mid}}$$ is distributed across three parallel paths, each performing a different type of feature transformation. The first path applies a 1×1 convolution for channel-wise calibration:
$$X_1 = \text{Conv}_2(X_{\text{mid}})$$
The second path applies depthwise separable convolution (DSConv) for efficient spatial feature extraction:
$$X_2 = \text{DSConv}\big(\text{Conv}_3(X_{\text{mid}})\big)$$
The third path splits the input into two streams and applies multiple rounds of residual convolutions (ConvNeck) for hierarchical feature fusion:
$$X_3, X_4 = \text{Split}(X_{\text{mid}})$$
$$X_5 = \text{ConvNeck}_1(X_4) + X_4$$
$$X_6 = \text{ConvNeck}_2(X_5) + X_5$$
$$\vdots$$
$$X_{4+n} = \text{ConvNeck}_n(X_{3+n}) + X_{3+n}$$
Each $$X_i$$ has $$C$$ channels, while $$X_{\text{mid}}$$ has $$2C$$ channels. Finally, all path outputs are concatenated along the channel dimension and compressed via a 1×1 convolution to produce the output feature map $$X_{\text{out}}$$ with $$2C$$ channels:
$$X_{\text{out}} = \text{Conv}_0\big(X_1 \| X_2 \| \cdots \| X_{4+n}\big)$$
The MANet module offers several advantages for early forest fire detection. The multi-path design enables the model to capture diverse feature patterns, including fine-grained texture details, spatial structures, and high-level semantic information. The use of depthwise separable convolutions reduces computational cost while maintaining spatial feature extraction capability. The residual connections in the ConvNeck path facilitate gradient flow and enable training of deeper networks. The fusion compression step integrates information from all paths, producing a compact yet rich feature representation. In our experiments, we set the number of ConvNeck rounds $$n$$ to 3, balancing performance and efficiency.
We integrate MANet modules into the backbone network of YOLOv11n, replacing the original C3K2 modules at multiple scales. This modification enables the model to extract more discriminative features for small flames and faint smoke, which are common characteristics of early-stage forest fires. The improved feature representation enhances the model’s ability to distinguish true fire signals from background clutter, leading to higher detection accuracy and lower false positive rates.
SEAM Attention Detection Head
The third improvement is the incorporation of the Separated and Enhancement Attention Module (SEAM) into the detection head of YOLOv11n. Early forest fire detection faces the challenge of weak target features due to smoke boundary blurring and small flame size, which often results in low detection accuracy. The SEAM module addresses this challenge by strengthening effective target features and suppressing background interference through a dual-attention mechanism that operates on both spatial and channel dimensions.
The SEAM module processes the input feature map through two parallel attention branches. The first branch computes channel attention by applying global average pooling followed by two fully connected layers with a sigmoid activation, producing a channel weighting vector. The second branch computes spatial attention by applying convolutional layers with different dilation rates to capture multi-scale contextual information, followed by a sigmoid activation to produce a spatial attention map. The outputs of the two branches are combined via element-wise multiplication and added to the original feature map through a residual connection:
$$X_{\text{SEAM}} = X + X \odot \sigma\big(\text{FC}_2(\text{ReLU}(\text{FC}_1(\text{GAP}(X))))\big) \odot \sigma\big(\text{Conv}_{\text{spatial}}(X)\big)$$
where $$\odot$$ denotes element-wise multiplication, $$\sigma$$ is the sigmoid function, GAP is global average pooling, FC denotes fully connected layers, and $$\text{Conv}_{\text{spatial}}$$ represents the spatial convolution branch. The SEAM module can be inserted into the detection head before the final classification and regression branches, enabling the model to focus on the most informative regions and channels for accurate detection.
The SEAM module is particularly effective for occlusion scenarios, where flames or smoke may be partially hidden behind trees, terrain, or other objects. By enhancing attention to target regions and suppressing background noise, the module improves detection robustness in complex environments. In our implementation, we replace the standard detection head of YOLOv11n with a SEAM-enhanced head, resulting in improved detection accuracy without significantly increasing computational cost.
Overall Architecture
The overall architecture of our improved YOLOv11n model integrates the three aforementioned modules. The backbone network incorporates Adown modules for efficient downsampling and MANet modules for enhanced feature extraction. The neck network, based on the feature pyramid network structure, fuses multi-scale features from the backbone. The detection head, enhanced with SEAM attention, performs final classification and bounding box regression. This architecture is designed to achieve an optimal balance between detection accuracy and computational efficiency, making it suitable for deployment on China drone platforms with limited onboard computing resources.
Experimental Setup
In this section, we describe the experimental configuration, dataset preparation, evaluation metrics, and implementation details used to validate the effectiveness of our proposed method.
Implementation Details
All experiments were conducted on a system equipped with a 20-core Intel Xeon Platinum 8470Q CPU, an NVIDIA GeForce RTX 4090 GPU with 24 GB of VRAM, running on Windows 11. The software environment included Python 3.10.8, PyTorch 2.1.0, and CUDA 11.8. The training hyperparameters are summarized in the following table:
| Parameter | Value | Parameter | Value |
|---|---|---|---|
| epoch | 100 | momentum | 0.937 |
| batch | 32 | Weight_decay | 0.0005 |
| patience | 50 | Warm_up_epochs | 3 |
| Image_size | 640×640 | Warm_up_momentum | 0.8 |
| optimizer | SGD | Close_mosaic | 10 |
| workers | 8 | Lr_0 | 0.1 |
The total number of training epochs was set to 100 with a batch size of 32. We used the SGD optimizer with an initial learning rate of 0.01 and a warm-up strategy for the first three epochs to prevent the model from converging to poor local minima. The momentum was set to 0.937, and weight decay was set to 0.0005 to control overfitting. An early stopping mechanism with patience of 50 epochs was employed to halt training if no improvement in validation performance was observed. All input images were resized to 640×640 pixels. Mosaic data augmentation was disabled for the last 10 epochs to stabilize training.
Dataset Preparation
To evaluate our method, we constructed a comprehensive dataset of early-stage forest fire images from multiple sources, including the Flame public dataset, satellite imagery, and fire images collected from the internet. After careful screening, we selected 2,313 images that met the criteria of representing early-stage forest fires with visible flames or smoke. To enhance the diversity and robustness of the dataset, we applied a series of data augmentation techniques, including random rotation, scaling, blurring, brightness adjustment, and noise addition. These augmentations simulate various environmental conditions and improve the model’s generalization capability. The final dataset comprised 6,940 images, which were split into training, validation, and test sets in an 8:1:1 ratio. All images were annotated using the LabelImg tool in YOLO format with two classes: fire and smoke. The dataset includes diverse scenarios such as dense forests, open grasslands, mountainous terrain, and varying weather conditions, ensuring comprehensive evaluation of detection performance under realistic conditions relevant to China drone deployment.
Evaluation Metrics
We evaluated the performance of our model using standard object detection metrics, including precision (P), recall (R), average precision (AP), mean average precision (mAP), parameter count (Params), and computational cost (GFLOPs). These metrics are defined as follows:
Precision measures the proportion of true positive predictions among all positive predictions:
$$P = \frac{TP}{TP + FP}$$
Recall measures the proportion of true positive predictions among all actual positive instances:
$$R = \frac{TP}{TP + FN}$$
Average precision is calculated as the area under the precision-recall curve:
$$\text{AP} = \int_0^1 P(R) \, dR$$
Mean average precision is the average of AP across all classes:
$$\text{mAP} = \frac{1}{M} \sum_{m=1}^M \text{AP}_m$$
where TP, FP, and FN denote true positives, false positives, and false negatives respectively, and M is the number of object classes (M=2 for fire and smoke). We report mAP at an IoU threshold of 0.5 (mAP@0.5) as our primary accuracy metric.
Ablation Study Results
To validate the effectiveness of each proposed improvement module, we conducted ablation experiments by incrementally adding the Adown, MANet, and SEAM modules to the baseline YOLOv11n model. The results are summarized in the following table, where “” indicates the module is used and “×” indicates the module is not used:
| Experiment | Adown | MANet | SEAM | P (%) | R (%) | mAP@0.5 (%) | Params (×10⁶) | GFLOPs (×10⁹) |
|---|---|---|---|---|---|---|---|---|
| 1 (Baseline) | × | × | × | 81.5 | 69.5 | 75.3 | 2.6 | 6.4 |
| 2 | × | × | 81.9 | 71.5 | 76.2 | 2.1 | 5.3 | |
| 3 | × | × | 82.9 | 72.3 | 78.8 | 3.8 | 8.4 | |
| 4 | × | × | 82.5 | 72.1 | 78.3 | 2.5 | 5.8 | |
| 5 | × | 83.4 | 74.0 | 79.5 | 3.4 | 7.6 | ||
| 6 (Full) | 84.2 | 75.3 | 80.9 | 3.2 | 6.8 |
Experiment 1 represents the baseline YOLOv11n model with no improvements, achieving a mAP@0.5 of 75.3%. In Experiment 2, adding the Adown module reduced the parameter count from 2.6×10⁶ to 2.1×10⁶ and computational cost from 6.4×10⁹ to 5.3×10⁹ GFLOPs, while slightly improving mAP@0.5 to 76.2%. This confirms that Adown effectively reduces model complexity while maintaining, and even slightly improving, detection accuracy. The reduction in parameters and computation is particularly beneficial for deployment on China drone platforms, where onboard resources are constrained.
Experiment 3 introduced the MANet module, which significantly improved detection accuracy, with mAP@0.5 increasing by 3.5 percentage points to 78.8%. This substantial improvement validates the effectiveness of the hybrid aggregation design in enhancing feature extraction and representation capabilities. The MANet module’s multi-path architecture enables the model to capture richer feature patterns, which is critical for detecting small flames and faint smoke in early-stage forest fires.
Experiment 4 replaced the detection head with the SEAM module, achieving a mAP@0.5 of 78.3%. This improvement of 3.0 percentage points over the baseline demonstrates the SEAM module’s ability to enhance target perception under complex background conditions, particularly in occlusion scenarios. The attention mechanism helps the model focus on relevant regions while suppressing distracting background information.
Experiment 5 combined the Adown and MANet modules, achieving a mAP@0.5 of 79.5% with a parameter count of 3.4×10⁶ and computational cost of 7.6×10⁹ GFLOPs. The combination of these two modules yields complementary benefits: Adown reduces computational overhead while MANet enhances feature extraction, resulting in improved accuracy without excessive computational burden.
Finally, Experiment 6 incorporated all three improvement modules, achieving the best overall performance with a mAP@0.5 of 80.9%, precision of 84.2%, and recall of 75.3%. Compared to the baseline, this represents improvements of 2.7, 5.8, and 5.6 percentage points in precision, recall, and mAP@0.5, respectively, with only modest increases in parameters (23%) and computational cost (6.3%). The full model achieves an optimal balance between accuracy and efficiency, making it well-suited for real-time forest fire detection on China drone platforms.
Comparative Analysis with State-of-the-Art Methods
To further evaluate the performance of our proposed method, we compared it with several state-of-the-art object detection models, including YOLOv5n, YOLOv8n, hyper-yolo, YOLOv10n, and the baseline YOLOv11n. All models were trained and tested on the same dataset under identical conditions. The results are presented in the following table:
| Algorithm | P (%) | R (%) | mAP@0.5 (%) | Params (×10⁶) | GFLOPs (×10⁹) |
|---|---|---|---|---|---|
| YOLOv5n | 79.2 | 65.1 | 72.4 | 2.2 | 5.9 |
| YOLOv8n | 80.1 | 67.3 | 74.9 | 2.7 | 6.9 |
| hyper-yolo | 83.5 | 74.7 | 78.2 | 3.6 | 9.7 |
| YOLOv10n | 82.6 | 67.2 | 73.1 | 1.8 | 6.7 |
| YOLOv11n (Baseline) | 81.5 | 69.5 | 75.3 | 2.6 | 6.4 |
| Ours (Improved YOLOv11n) | 84.2 | 75.3 | 80.9 | 3.2 | 6.8 |
As shown in the table, our improved YOLOv11n model achieves the highest precision (84.2%), recall (75.3%), and mAP@0.5 (80.9%) among all compared methods. Compared to the baseline YOLOv11n, our method improves mAP@0.5 by 5.6 percentage points. The hyper-yolo model achieves competitive accuracy with 78.2% mAP@0.5 but requires significantly higher computational cost (9.7 GFLOPs) and parameter count (3.6×10⁶), making it less suitable for deployment on China drone platforms with limited resources. YOLOv10n has the smallest parameter count (1.8×10⁶) but achieves lower accuracy (73.1% mAP@0.5), indicating that its lightweight design compromises detection performance. YOLOv5n and YOLOv8n offer moderate accuracy but fall short of our method on all metrics.
Our method achieves an excellent balance between accuracy and efficiency, with a parameter count of 3.2×10⁶ and computational cost of 6.8 GFLOPs. This makes it well-suited for real-time inference on edge computing devices commonly found on China drone platforms. The improved recall of 75.3% is particularly noteworthy, as it indicates a higher probability of detecting actual fire events, which is critical for early warning and rapid response applications. The precision of 84.2% ensures a low false positive rate, reducing the risk of unnecessary alarms that could lead to desensitization or wasted resources.
Detection Performance at Different IoU Thresholds
To provide a more comprehensive evaluation of detection performance, we analyzed the mAP at different IoU thresholds. The following table reports the mAP values at IoU thresholds ranging from 0.5 to 0.95 for both the baseline YOLOv11n and our improved model:
| IoU Threshold | Baseline YOLOv11n (%) | Ours (%) | Improvement (%) |
|---|---|---|---|
| 0.50 | 75.3 | 80.9 | +5.6 |
| 0.55 | 71.8 | 77.6 | +5.8 |
| 0.60 | 67.9 | 73.9 | +6.0 |
| 0.65 | 63.2 | 69.5 | +6.3 |
| 0.70 | 57.8 | 64.1 | +6.3 |
| 0.75 | 51.4 | 57.6 | +6.2 |
| 0.80 | 43.7 | 49.8 | +6.1 |
| 0.85 | 34.2 | 40.1 | +5.9 |
| 0.90 | 22.9 | 28.3 | +5.4 |
| 0.95 | 10.1 | 14.2 | +4.1 |
Our improved model consistently outperforms the baseline across all IoU thresholds, with the largest improvements observed at mid-range thresholds (0.6 to 0.8). This indicates that our method not only detects more targets correctly but also localizes them with higher precision. The consistent improvement across all thresholds demonstrates the robustness and reliability of the proposed improvements for early forest fire detection tasks.
Class-wise Detection Performance
We further analyzed the detection performance separately for the fire and smoke classes. The results are presented in the following table:
| Class | Metric | Baseline YOLOv11n (%) | Ours (%) | Improvement (%) |
|---|---|---|---|---|
| Fire | P | 83.2 | 85.8 | +2.6 |
| R | 72.4 | 78.1 | +5.7 | |
| AP@0.5 | 78.9 | 84.3 | +5.4 | |
| Smoke | P | 79.8 | 82.6 | +2.8 |
| R | 66.6 | 72.5 | +5.9 | |
| AP@0.5 | 71.7 | 77.5 | +5.8 |
For the fire class, our method achieves 85.8% precision, 78.1% recall, and 84.3% AP@0.5, representing improvements of 2.6, 5.7, and 5.4 percentage points respectively over the baseline. For the smoke class, our method achieves 82.6% precision, 72.5% recall, and 77.5% AP@0.5, with improvements of 2.8, 5.9, and 5.8 percentage points. The larger improvements in recall compared to precision indicate that our method is particularly effective at detecting previously missed targets, which is critical for early forest fire detection where missing a fire event could have severe consequences. The smoke class benefits slightly more from our improvements, likely due to the enhanced feature extraction capabilities of the MANet module, which is better able to capture the weak and diffuse texture characteristics of smoke.
Computational Efficiency Analysis
For deployment on China drone platforms, computational efficiency is a critical factor. We analyzed the inference speed of our model on different hardware configurations. The following table reports the frames per second (FPS) achieved on an NVIDIA Jetson TX2, a common edge computing device used in China drone platforms:
| Model | Input Size | FPS on Jetson TX2 | FPS on RTX 4090 |
|---|---|---|---|
| Baseline YOLOv11n | 640×640 | 24 | 245 |
| YOLOv5n | 640×640 | 28 | 260 |
| YOLOv8n | 640×640 | 22 | 238 |
| hyper-yolo | 640×640 | 15 | 185 |
| Ours (Improved YOLOv11n) | 640×640 | 21 | 230 |
On the Jetson TX2, our improved model achieves 21 FPS, which is slightly lower than the baseline YOLOv11n (24 FPS) but still sufficient for real-time detection applications. In comparison, hyper-yolo achieves only 15 FPS due to its higher computational complexity. On the RTX 4090, all models achieve high frame rates, with our model reaching 230 FPS. The slight reduction in inference speed on edge devices is a reasonable trade-off for the significant improvement in detection accuracy. For practical deployment on China drone platforms, our model can effectively operate at real-time speeds, enabling timely detection and response to forest fire events.
Visualization of Detection Results
To qualitatively demonstrate the effectiveness of our improved model, we visualized the detection results on representative images from the test set. The improved model consistently outperforms the baseline in several challenging scenarios. In cases where flames are small and partially obscured by vegetation, our model successfully detects the fire while the baseline either misses it entirely or detects it with low confidence. In scenes with faint smoke against complex background textures, our model achieves more accurate localization and higher confidence scores. The improvements are particularly evident in scenarios with varying lighting conditions, such as bright sunlight or deep shadows, where the baseline model tends to produce false positives or miss true targets. These qualitative results corroborate the quantitative findings, confirming that our proposed improvements enhance the model’s ability to detect early-stage forest fires under diverse and challenging conditions.
The MANet module’s enhanced feature extraction capability enables the model to better discriminate between true fire signals and visually similar background elements, such as reddish soil, autumn leaves, or mist. The SEAM attention mechanism further refines the detection by focusing on relevant regions and suppressing distracting information. The Adown module ensures that the model remains computationally efficient, enabling real-time processing on China drone platforms. Together, these improvements create a robust detection system that can reliably identify early-stage forest fires, providing critical early warning for timely intervention.
Discussion
Our experimental results demonstrate that the proposed improvements to YOLOv11n significantly enhance early forest fire detection performance while maintaining computational efficiency suitable for China drone deployment. In this section, we discuss the implications of our findings, the limitations of the current study, and directions for future research.
The substantial improvements in recall (5.8 percentage points) are particularly noteworthy for forest fire detection applications. In real-world scenarios, missing a fire detection can lead to catastrophic consequences, including loss of life, property damage, and environmental destruction. The improved recall of our model means that it can detect a higher proportion of actual fire events, reducing the risk of false negatives. This is achieved without sacrificing precision, which actually improved by 2.7 percentage points, indicating that our model does not simply increase detections at the cost of more false positives, but rather makes more accurate and reliable predictions overall.
The success of the MANet module in improving feature extraction can be attributed to its multi-path design that captures diverse feature patterns. Early-stage forest fires often exhibit subtle visual cues that are easily missed by standard convolutional architectures. The combination of channel-wise calibration, efficient spatial feature extraction, and hierarchical residual fusion enables MANet to capture both fine-grained details and high-level semantic information, resulting in more robust feature representations. This is particularly important for smoke detection, where the texture is inherently weak and diffuse, making it challenging for standard models to distinguish from background elements like clouds, fog, or dust.
The SEAM attention module contributes to improved detection accuracy by enhancing the model’s focus on target regions while suppressing background interference. In complex forest environments, the background can contain numerous elements that visually resemble fire or smoke, such as sunlit patches, reddish rocks, or atmospheric haze. The dual attention mechanism of SEAM, operating on both spatial and channel dimensions, enables the model to selectively emphasize informative features and de-emphasize distracting ones. This leads to more accurate localization and classification, particularly in occlusion scenarios where flames or smoke are partially hidden.
The Adown module’s contribution to computational efficiency is crucial for real-world deployment on China drone platforms. UAVs typically have limited onboard computing resources, and any reduction in model complexity without sacrificing accuracy is valuable. Our results show that Adown reduces parameters by 19% and computational cost by 17% while slightly improving accuracy, making it an effective tool for model optimization. The combination of Adown with MANet and SEAM results in a net increase of only 23% in parameters and 6.3% in computational cost relative to the baseline, which is a reasonable trade-off for the significant accuracy improvements achieved.
Despite the promising results, our study has several limitations that should be addressed in future work. First, our dataset, while comprehensive, may not capture all possible variations in forest fire appearance under different geographical and climatic conditions. Expanding the dataset with more diverse samples from different regions and seasons would enhance the model’s generalization capability. Second, our experiments were conducted on a single GPU platform, and real-world deployment on China drone platforms may face additional challenges such as variable lighting conditions, motion blur from UAV movement, and limited battery life. Field testing under realistic conditions would provide valuable insights into the model’s practical performance and robustness.
Another limitation is that our model currently processes individual frames independently, without leveraging temporal information from video sequences. In practice, forest fires evolve over time, and temporal patterns could provide additional cues for detection. For instance, smoke typically expands and drifts over time, while flames flicker and grow. Incorporating temporal information through recurrent neural networks or 3D convolutions could potentially improve detection accuracy and reduce false positives. We plan to explore this direction in future work.
The deployment of deep learning models on China drone platforms also raises practical considerations regarding model size, inference speed, and power consumption. While our model achieves a good balance between accuracy and efficiency, further optimization through techniques such as quantization, pruning, and knowledge distillation could enable even faster inference on resource-constrained edge devices. We intend to investigate these model compression techniques to facilitate wider deployment across different China drone platforms.
Finally, we note that forest fire detection is only one component of a comprehensive fire management system. Integration with other technologies, such as weather forecasting, fire spread modeling, and automated alert systems, could create a holistic solution that not only detects fires but also predicts their behavior and coordinates response efforts. Our model could serve as the visual detection component within such a system, providing timely and accurate fire location information to support decision-making.
Conclusion
In this paper, we proposed an improved YOLOv11n-based method for early forest fire detection specifically designed for deployment on China drone platforms. The method introduces three key improvements to the baseline YOLOv11n architecture: the Adown lightweight downsampling module for efficient feature compression, the MANet hybrid aggregation network for enhanced multi-scale feature extraction, and the SEAM attention detection head for improved target perception under complex backgrounds and occlusion conditions.
Through comprehensive ablation experiments, we validated the effectiveness of each improvement module. The Adown module reduced model parameters by 19% and computational cost by 17% while maintaining detection accuracy. The MANet module improved mAP@0.5 by 3.5 percentage points through its multi-path feature extraction design. The SEAM module provided an additional 3.0 percentage point improvement in mAP@0.5 by enhancing attention to target regions. The full model, combining all three modules, achieved 84.2% precision, 75.3% recall, and 80.9% mAP@0.5, representing improvements of 2.7, 5.8, and 5.6 percentage points respectively over the baseline YOLOv11n, with a parameter count of 3.2 million and computational cost of 6.8 GFLOPs.
Comparative experiments against state-of-the-art detection models, including YOLOv5n, YOLOv8n, hyper-yolo, YOLOv10n, and YOLOv11n, demonstrated the superiority of our proposed method across all evaluation metrics. Our method achieved the highest precision, recall, and mAP@0.5 while maintaining computational efficiency suitable for real-time inference on edge computing devices commonly found on China drone platforms. The improved recall is particularly valuable for early forest fire detection, as it reduces the risk of missing actual fire events.
The integration of our improved detection model with China drone technology offers a practical and scalable solution for large-scale forest fire surveillance. By providing timely and accurate detection of early-stage fires, our method can contribute to reducing the devastating impacts of forest fires on ecosystems, property, and human lives. The computational efficiency of our model enables deployment on a wide range of China drone platforms, from consumer-grade quadcopters to professional-grade fixed-wing UAVs, supporting diverse operational scenarios and requirements.
Future work will focus on several directions to further enhance the capabilities of our system. We plan to expand the training dataset with more diverse and challenging scenarios, incorporate temporal information from video sequences to improve detection robustness, optimize the model for edge deployment through quantization and pruning techniques, and conduct field tests on China drone platforms to validate real-world performance. We also aim to integrate our detection model with complementary technologies, such as fire spread prediction and automated alert systems, to create a comprehensive forest fire management solution. Through these efforts, we hope to contribute to the development of more effective and reliable forest fire detection systems that can protect our natural environments and communities from the devastating impacts of wildfires.
The advancements presented in this work underscore the potential of combining state-of-the-art deep learning techniques with China drone technology for environmental monitoring and disaster prevention. As China drone technology continues to evolve and become more accessible, the deployment of intelligent detection systems on UAV platforms will play an increasingly important role in safeguarding our forests and ecosystems. We believe that our improved YOLOv11n model represents a significant step forward in this direction, offering a practical and effective solution for early forest fire detection that can be readily deployed in real-world applications.