Surface Damage Detection in Hydraulic Structures from UAV Images Using Lightweight Neural Networks
Timely and accurate identification of surface damage in hydraulic structures is essential for maintaining structural integrity and ensuring operational safety. Traditional manual inspections are time-consuming, labor-intensive, and prone to subjectivity, especially for large-scale or inaccessible in...
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MDPI AG
2025-08-01
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| author | Feng Han Chongshi Gu |
| author_facet | Feng Han Chongshi Gu |
| author_sort | Feng Han |
| collection | DOAJ |
| description | Timely and accurate identification of surface damage in hydraulic structures is essential for maintaining structural integrity and ensuring operational safety. Traditional manual inspections are time-consuming, labor-intensive, and prone to subjectivity, especially for large-scale or inaccessible infrastructure. Leveraging advancements in aerial imaging, unmanned aerial vehicles (UAVs) enable efficient acquisition of high-resolution visual data across expansive hydraulic environments. However, existing deep learning (DL) models often lack architectural adaptations for the visual complexities of UAV imagery, including low-texture contrast, noise interference, and irregular crack patterns. To address these challenges, this study proposes a lightweight, robust, and high-precision segmentation framework, called LFPA-EAM-Fast-SCNN, specifically designed for pixel-level damage detection in UAV-captured images of hydraulic concrete surfaces. The developed DL-based model integrates an enhanced Fast-SCNN backbone for efficient feature extraction, a Lightweight Feature Pyramid Attention (LFPA) module for multi-scale context enhancement, and an Edge Attention Module (EAM) for refined boundary localization. The experimental results on a custom UAV-based dataset show that the proposed damage detection method achieves superior performance, with a precision of 0.949, a recall of 0.892, an F1 score of 0.906, and an IoU of 87.92%, outperforming U-Net, Attention U-Net, SegNet, DeepLab v3+, I-ST-UNet, and SegFormer. Additionally, it reaches a real-time inference speed of 56.31 FPS, significantly surpassing other models. The experimental results demonstrate the proposed framework’s strong generalization capability and robustness under varying noise levels and damage scenarios, underscoring its suitability for scalable, automated surface damage assessment in UAV-based remote sensing of civil infrastructure. |
| format | Article |
| id | doaj-art-d09b6d57f7c3412f83a240eafca54e02 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
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| series | Remote Sensing |
| spelling | doaj-art-d09b6d57f7c3412f83a240eafca54e022025-08-20T03:36:22ZengMDPI AGRemote Sensing2072-42922025-08-011715266810.3390/rs17152668Surface Damage Detection in Hydraulic Structures from UAV Images Using Lightweight Neural NetworksFeng Han0Chongshi Gu1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, ChinaState Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, ChinaTimely and accurate identification of surface damage in hydraulic structures is essential for maintaining structural integrity and ensuring operational safety. Traditional manual inspections are time-consuming, labor-intensive, and prone to subjectivity, especially for large-scale or inaccessible infrastructure. Leveraging advancements in aerial imaging, unmanned aerial vehicles (UAVs) enable efficient acquisition of high-resolution visual data across expansive hydraulic environments. However, existing deep learning (DL) models often lack architectural adaptations for the visual complexities of UAV imagery, including low-texture contrast, noise interference, and irregular crack patterns. To address these challenges, this study proposes a lightweight, robust, and high-precision segmentation framework, called LFPA-EAM-Fast-SCNN, specifically designed for pixel-level damage detection in UAV-captured images of hydraulic concrete surfaces. The developed DL-based model integrates an enhanced Fast-SCNN backbone for efficient feature extraction, a Lightweight Feature Pyramid Attention (LFPA) module for multi-scale context enhancement, and an Edge Attention Module (EAM) for refined boundary localization. The experimental results on a custom UAV-based dataset show that the proposed damage detection method achieves superior performance, with a precision of 0.949, a recall of 0.892, an F1 score of 0.906, and an IoU of 87.92%, outperforming U-Net, Attention U-Net, SegNet, DeepLab v3+, I-ST-UNet, and SegFormer. Additionally, it reaches a real-time inference speed of 56.31 FPS, significantly surpassing other models. The experimental results demonstrate the proposed framework’s strong generalization capability and robustness under varying noise levels and damage scenarios, underscoring its suitability for scalable, automated surface damage assessment in UAV-based remote sensing of civil infrastructure.https://www.mdpi.com/2072-4292/17/15/2668hydraulic buildingsUAV inspectiondeep learningCNNdamage detectioncrack segmentation |
| spellingShingle | Feng Han Chongshi Gu Surface Damage Detection in Hydraulic Structures from UAV Images Using Lightweight Neural Networks Remote Sensing hydraulic buildings UAV inspection deep learning CNN damage detection crack segmentation |
| title | Surface Damage Detection in Hydraulic Structures from UAV Images Using Lightweight Neural Networks |
| title_full | Surface Damage Detection in Hydraulic Structures from UAV Images Using Lightweight Neural Networks |
| title_fullStr | Surface Damage Detection in Hydraulic Structures from UAV Images Using Lightweight Neural Networks |
| title_full_unstemmed | Surface Damage Detection in Hydraulic Structures from UAV Images Using Lightweight Neural Networks |
| title_short | Surface Damage Detection in Hydraulic Structures from UAV Images Using Lightweight Neural Networks |
| title_sort | surface damage detection in hydraulic structures from uav images using lightweight neural networks |
| topic | hydraulic buildings UAV inspection deep learning CNN damage detection crack segmentation |
| url | https://www.mdpi.com/2072-4292/17/15/2668 |
| work_keys_str_mv | AT fenghan surfacedamagedetectioninhydraulicstructuresfromuavimagesusinglightweightneuralnetworks AT chongshigu surfacedamagedetectioninhydraulicstructuresfromuavimagesusinglightweightneuralnetworks |