Multi-Scale Crack Detection and Quantification of Concrete Bridges Based on Aerial Photography and Improved Object Detection Network
Regular crack detection is essential for extending the service life of bridges. However, the image data collected during bridge crack inspections are complex to convert into physical information and construct intuitive and comprehensive Three-Dimensional (3D) models incorporating crack information....
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MDPI AG
2025-03-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/7/1117 |
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| author | Liming Zhou Haowen Jia Shang Jiang Fei Xu Hao Tang Chao Xiang Guoqing Wang Hemin Zheng Lingkun Chen |
| author_facet | Liming Zhou Haowen Jia Shang Jiang Fei Xu Hao Tang Chao Xiang Guoqing Wang Hemin Zheng Lingkun Chen |
| author_sort | Liming Zhou |
| collection | DOAJ |
| description | Regular crack detection is essential for extending the service life of bridges. However, the image data collected during bridge crack inspections are complex to convert into physical information and construct intuitive and comprehensive Three-Dimensional (3D) models incorporating crack information. An intelligent crack detection method for bridge surface damage based on Unmanned Aerial Vehicles (UAVs) is proposed for these challenges, incorporating a three-stage detection, quantification, and visualization process. This method enables automatic crack detection, quantification, and localization in a 3D model, generating a bridge model that includes crack details and distribution. The key contributions of this method are as follows: (1) The DCN-BiFPN-EMA-YOLO (DBE-YOLO) crack detection network is introduced, which improves the model’s ability to extract crack features from complex backgrounds and enhances its multi-scale detection capability for accurate detection; (2) a more comprehensive crack quantification method is proposed, integrating the crack automation detection system for accurate crack quantification and efficient processing; (3) crack information is mapped onto the 3D model by computing the camera pose for each image in the 3D model for intuitive crack visualization. Experimental results from tests on a concrete beam and an urban bridge demonstrate that the proposed method accurately identifies and quantifies crack images captured by UAVs. The DBE-YOLO network achieves an accuracy of 96.79% and an F1 score of 88.51%, improving accuracy by 3.19% and the F1 score by 3.8% compared to the original model. The quantification accuracy is within 10% of the error margin of traditional manual inspection. A 3D bridge model was also constructed and integrated with crack information. |
| format | Article |
| id | doaj-art-64a67212ce99450c9b7a11f8f38f535d |
| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-64a67212ce99450c9b7a11f8f38f535d2025-08-20T03:06:32ZengMDPI AGBuildings2075-53092025-03-01157111710.3390/buildings15071117Multi-Scale Crack Detection and Quantification of Concrete Bridges Based on Aerial Photography and Improved Object Detection NetworkLiming Zhou0Haowen Jia1Shang Jiang2Fei Xu3Hao Tang4Chao Xiang5Guoqing Wang6Hemin Zheng7Lingkun Chen8School of Safety Engineering and Emergency Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaSchool of Safety Engineering and Emergency Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaSchool of Transportation and Civil Engineering, Nantong University, Nantong 226019, ChinaKey Laboratory of Large Structural Health Monitoring and Control, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaSchool of Safety Engineering and Emergency Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaSchool of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaYanzhao Modern Transportation Laboratory, Shijiazhuang 050043, ChinaChina Railway Design Group Co., Ltd., Tianjin 300308, ChinaCollege of Architecture Science and Engineering, Yangzhou University, Yangzhou 225127, ChinaRegular crack detection is essential for extending the service life of bridges. However, the image data collected during bridge crack inspections are complex to convert into physical information and construct intuitive and comprehensive Three-Dimensional (3D) models incorporating crack information. An intelligent crack detection method for bridge surface damage based on Unmanned Aerial Vehicles (UAVs) is proposed for these challenges, incorporating a three-stage detection, quantification, and visualization process. This method enables automatic crack detection, quantification, and localization in a 3D model, generating a bridge model that includes crack details and distribution. The key contributions of this method are as follows: (1) The DCN-BiFPN-EMA-YOLO (DBE-YOLO) crack detection network is introduced, which improves the model’s ability to extract crack features from complex backgrounds and enhances its multi-scale detection capability for accurate detection; (2) a more comprehensive crack quantification method is proposed, integrating the crack automation detection system for accurate crack quantification and efficient processing; (3) crack information is mapped onto the 3D model by computing the camera pose for each image in the 3D model for intuitive crack visualization. Experimental results from tests on a concrete beam and an urban bridge demonstrate that the proposed method accurately identifies and quantifies crack images captured by UAVs. The DBE-YOLO network achieves an accuracy of 96.79% and an F1 score of 88.51%, improving accuracy by 3.19% and the F1 score by 3.8% compared to the original model. The quantification accuracy is within 10% of the error margin of traditional manual inspection. A 3D bridge model was also constructed and integrated with crack information.https://www.mdpi.com/2075-5309/15/7/1117bridge inspectioncrack detectiondeep learningUAV vision |
| spellingShingle | Liming Zhou Haowen Jia Shang Jiang Fei Xu Hao Tang Chao Xiang Guoqing Wang Hemin Zheng Lingkun Chen Multi-Scale Crack Detection and Quantification of Concrete Bridges Based on Aerial Photography and Improved Object Detection Network Buildings bridge inspection crack detection deep learning UAV vision |
| title | Multi-Scale Crack Detection and Quantification of Concrete Bridges Based on Aerial Photography and Improved Object Detection Network |
| title_full | Multi-Scale Crack Detection and Quantification of Concrete Bridges Based on Aerial Photography and Improved Object Detection Network |
| title_fullStr | Multi-Scale Crack Detection and Quantification of Concrete Bridges Based on Aerial Photography and Improved Object Detection Network |
| title_full_unstemmed | Multi-Scale Crack Detection and Quantification of Concrete Bridges Based on Aerial Photography and Improved Object Detection Network |
| title_short | Multi-Scale Crack Detection and Quantification of Concrete Bridges Based on Aerial Photography and Improved Object Detection Network |
| title_sort | multi scale crack detection and quantification of concrete bridges based on aerial photography and improved object detection network |
| topic | bridge inspection crack detection deep learning UAV vision |
| url | https://www.mdpi.com/2075-5309/15/7/1117 |
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