In modern hydraulic engineering, the integrity of control gate structures is paramount for regulating water flow, managing flood risks, and preserving ecological balance. Concrete, as a primary material in these gates, is susceptible to various surface defects such as cracks, spalling, and corrosion, which can compromise structural stability and operational efficiency. Traditional inspection methods often rely on manual assessments, which are time-consuming, prone to human error, and limited in coverage. To address these challenges, I propose an innovative approach that leverages drone technology for image acquisition and enhancement, enabling precise and automated identification of concrete surface defects. This method integrates advanced image processing algorithms, including Laplacian-based enhancement and semantic fusion, to extract high-quality features and classify defects under diverse environmental conditions. By utilizing Unmanned Aerial Vehicles (UAVs), the system achieves comprehensive spatial coverage and real-time data transmission, significantly improving the accuracy and efficiency of defect recognition. In this article, I detail the methodology, experimental setup, and results, emphasizing the role of drone technology in transforming infrastructure monitoring. The following sections explore the image enhancement process, feature extraction techniques, and classification mechanisms, supported by mathematical formulations and empirical data to validate the approach’s robustness and reliability.
The foundation of this method lies in the deployment of Unmanned Aerial Vehicles for high-resolution image capture. These drones are equipped with specialized cameras and software, such as Context Capture, to facilitate automated data transmission and spatial avoidance. The image acquisition process involves defining a规避空间 to exclude irrelevant areas and focus on defect-prone regions. Mathematically, this space is represented as a set of coordinates: $$\{x, y, z \mid x_{\text{min}} < x < x_{\text{max}}, y_{\text{min}} < y < y_{\text{max}}, z_{\text{min}} < z < z_{\text{max}}\},$$ where $(x, y, z)$ denote the采集点 coordinates for defect identification. This ensures that the drone navigates efficiently, minimizing collisions and optimizing data collection. The collected images are then subjected to augmentation to enhance their quality. An affine transformation matrix $M$ is applied to adjust spatial orientations: $$M = \begin{bmatrix} \cos\theta & \sin\theta & 0 \\ -\sin\theta & \cos\theta & 0 \\ 0 & 0 & 1 \end{bmatrix},$$ where $\theta$ is the rotation angle for data augmentation. This step is crucial for handling variations in lighting and perspective, which are common in outdoor environments where drone technology operates.
To further improve image clarity, I employ a Laplacian-based enhancement algorithm. This involves computing the gradient operator $\nabla f$ for sharpening: $$\nabla f = \frac{\partial f}{\partial x} + \frac{\partial f}{\partial y},$$ where $\partial f$ represents differential parameters, and $\partial x f$ and $\partial y f$ correspond to horizontal and vertical filtering information, respectively. The enhancement function $I$ is derived as: $$I = \frac{\max(X)}{\lg(\max(X) + 1)} \times \nabla f,$$ with $X$ as the input image and $\times$ denoting the enhancement multiplier. This function amplifies high-frequency components, highlighting defect edges and textures. Subsequently, bilateral filtering is applied to reduce noise while preserving details, resulting in the enhanced image $g(x, y)$: $$g(x, y) = [f(x, y) \oplus b(x, y) – f(x, y) \Theta b(x, y)] \times I,$$ where $b(x, y)$ is an edge enhancement element, and $\oplus$ and $\Theta$ represent morphological operations for dilation and erosion. From this enhanced image, high-quality defect features $D_B$ are extracted using gradient reconstruction: $$D_B = \min[g(x, y) \oplus B, r],$$ where $B$ is a structural element, and $r$ is a texture parameter. This process ensures that even subtle defects are captured, demonstrating the efficacy of drone technology in generating reliable data for analysis.
Following feature extraction, the classification phase employs a Transformer-based encoder integrated with a semantic fusion module. This module addresses scale variations and semantic alignment issues by performing long-range regression and multi-scale feature adjustment. The Transformer encoder processes the features $D_B$ through self-attention mechanisms, capturing contextual relationships for accurate defect categorization. The semantic fusion module combines features from different scales, enhancing the model’s ability to distinguish between similar defect types, such as cracks and spalling. This is achieved by setting dynamic classification thresholds and optimizing feature differences, ensuring robust performance under challenging conditions like uneven illumination or partial occlusions. The entire workflow, from drone-based image acquisition to classification, is designed to handle real-world scenarios, making Unmanned Aerial Vehicles indispensable for modern infrastructure inspection.
To validate the proposed method, I conducted extensive experiments using a dataset comprising 8,200 annotated images captured under various lighting conditions (e.g., bright sunlight, overcast skies, and low-light environments) and concrete surface states (e.g., dry/wet and rough/smooth textures). The dataset included three primary defect categories: cracks (42%, with sub-types like transverse, longitudinal, and网状), spalling (35%, including shallow and deep damage), and corrosion (20%, featuring点状 and片状 patterns), along with 3% defect-free samples for negative examples. The experimental setup utilized a PyTorch framework on an Intel Xeon Gold server, with AdamW optimizer and Cosine Annealing for learning rate adjustment. Key performance metrics, such as the Intersection over Union (IOU), were computed as: $$\text{IOU} = \frac{\text{TP}}{\text{TP} + \text{FN} + \text{FP}},$$ where TP, FN, and FP denote true positives, false negatives, and false positives, respectively. The drone technology employed an RS camera with parameters summarized in Table 1, ensuring high-quality image capture across diverse scenarios.
| Parameter | Value |
|---|---|
| Illumination | Passive lighting (excluding light source) |
| Spectral method | Transmission grating |
| Spectral range | 900–1700 nm |
| Spectral bands | 254 |
| Spectral resolution | 8 nm |
| Slit width | 25 μm |
| Transmission efficiency | >60% |
| Stray light | <0.5% |
| Number of spatial pixels | 320 |
| Pixel size | 30 μm |
| Imaging speed | 200 Hz |
| Detector | InGaAs |
The experimental results demonstrated the method’s effectiveness through ablation studies, scenario variations, and IOU performance comparisons. In ablation tests, the proposed approach achieved precise defect segmentation across multiple images (e.g., DU-1 to DU-6), with clear boundaries and no resource constraints. For scenario variations, including uneven lighting, reagent obstructions, and physical遮挡, the method maintained high recognition accuracy, showcasing its robustness and generalization capability. The IOU values were consistently high, ranging from 85% to 95% across different iteration cycles, with a standard deviation of ≤3%. This outperformed comparative methods, such as visual-driven approaches (72–88% IOU, SD 6.5%) and optimized variational mode decomposition (69–75% IOU, SD 4.2%), highlighting the superiority of integrating drone technology with advanced image enhancement. The semantic fusion module played a critical role in this success by adaptively weighting multi-scale features and dynamically adjusting classification thresholds, as illustrated in the following formula for feature integration: $$F_{\text{fused}} = \sum_{i=1}^{n} w_i \cdot F_i,$$ where $w_i$ represents the weight for feature $F_i$ at scale $i$, and $n$ is the number of scales. This ensures that defects of varying sizes are accurately identified, reinforcing the value of Unmanned Aerial Vehicles in complex monitoring tasks.
In conclusion, the integration of drone technology into concrete surface defect identification for control gates offers a transformative solution for hydraulic infrastructure maintenance. By combining UAV-based image acquisition with Laplacian enhancement and semantic fusion classification, this method achieves high precision and reliability under diverse environmental conditions. The experimental validation confirms its ability to handle challenges such as occlusion and lighting variations, with IOU scores underscoring its effectiveness. Future work could explore real-time implementation and expansion to other structural types, further solidifying the role of Unmanned Aerial Vehicles in smart infrastructure management. This approach not only enhances operational safety but also reduces inspection costs, demonstrating the profound impact of drone technology on sustainable engineering practices.
The mathematical formulations and algorithmic steps detailed herein provide a comprehensive framework for replicating and extending this research. For instance, the enhancement process can be generalized to other applications by adjusting parameters like the gradient operator or fusion weights. Additionally, the use of drone technology ensures scalability across large-scale projects, such as dam or bridge inspections, where manual methods are impractical. As Unmanned Aerial Vehicles continue to evolve, their integration with machine learning models will undoubtedly drive innovations in autonomous monitoring, making this method a cornerstone for future advancements in civil engineering and asset management.