Study on Lightweight Bridge Crack Detection Algorithm Based on YOLO11
Bridge crack detection is a key factor in ensuring the safety and extending the lifespan of bridges. Traditional detection methods often suffer from low efficiency and insufficient accuracy. The development of computer vision has gradually made bridge crack detection methods based on deep learning t...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/11/3276 |
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| author | Xuwei Dong Jiashuo Yuan Jinpeng Dai |
| author_facet | Xuwei Dong Jiashuo Yuan Jinpeng Dai |
| author_sort | Xuwei Dong |
| collection | DOAJ |
| description | Bridge crack detection is a key factor in ensuring the safety and extending the lifespan of bridges. Traditional detection methods often suffer from low efficiency and insufficient accuracy. The development of computer vision has gradually made bridge crack detection methods based on deep learning to become a research hotspot. In this study, a lightweight bridge crack detection algorithm, YOLO11-Bridge Detection (YOLO11-BD), is proposed based on the optimization of the YOLO11 model. This algorithm uses an efficient multiscale conv all (EMSCA) module to enhance channel and spatial attention, thereby strengthening its ability to extract crack features. Additionally, the algorithm improves detection accuracy without increasing the model size. Furthermore, a lightweight detection head (LDH) is introduced to process feature information from different channels using efficient grouped convolutions. It reduces the model’s parameters and computations whilst preserving accuracy, thereby achieving a lightweight model. Experimental results show that compared with the original YOLO11, the YOLO11-BD algorithm improves mAP50 and mAP50-95 on the bridge crack dataset by 3.1% and 4.8%, respectively, whilst significantly reducing GFLOPs by 19.05%. Its frame per second remains higher than 500, demonstrating excellent real-time detection capability and high computational efficiency. The algorithm proposed in this study provides an efficient and flexible solution for the monitoring of bridge cracks using remote sensing devices such as drones, and it has significant practical application value. Its lightweight design ensures strong cross-platform adaptability and provides reliable technical support for intelligent bridge management and maintenance. |
| format | Article |
| id | doaj-art-6de87eb8b6594c9fb8dfb53802c95f9d |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-6de87eb8b6594c9fb8dfb53802c95f9d2025-08-20T02:23:05ZengMDPI AGSensors1424-82202025-05-012511327610.3390/s25113276Study on Lightweight Bridge Crack Detection Algorithm Based on YOLO11Xuwei Dong0Jiashuo Yuan1Jinpeng Dai2Key Laboratory of Opto-Electronic Technology and Intelligent Control, Ministry of Education, Lanzhou Jiaotong University, Lanzhou 730070, ChinaKey Laboratory of Opto-Electronic Technology and Intelligent Control, Ministry of Education, Lanzhou Jiaotong University, Lanzhou 730070, ChinaNational and Provincial Joint Engineering Laboratory of Road & Bridge Disaster Prevention and Control, Lanzhou Jiaotong University, Lanzhou 730070, ChinaBridge crack detection is a key factor in ensuring the safety and extending the lifespan of bridges. Traditional detection methods often suffer from low efficiency and insufficient accuracy. The development of computer vision has gradually made bridge crack detection methods based on deep learning to become a research hotspot. In this study, a lightweight bridge crack detection algorithm, YOLO11-Bridge Detection (YOLO11-BD), is proposed based on the optimization of the YOLO11 model. This algorithm uses an efficient multiscale conv all (EMSCA) module to enhance channel and spatial attention, thereby strengthening its ability to extract crack features. Additionally, the algorithm improves detection accuracy without increasing the model size. Furthermore, a lightweight detection head (LDH) is introduced to process feature information from different channels using efficient grouped convolutions. It reduces the model’s parameters and computations whilst preserving accuracy, thereby achieving a lightweight model. Experimental results show that compared with the original YOLO11, the YOLO11-BD algorithm improves mAP50 and mAP50-95 on the bridge crack dataset by 3.1% and 4.8%, respectively, whilst significantly reducing GFLOPs by 19.05%. Its frame per second remains higher than 500, demonstrating excellent real-time detection capability and high computational efficiency. The algorithm proposed in this study provides an efficient and flexible solution for the monitoring of bridge cracks using remote sensing devices such as drones, and it has significant practical application value. Its lightweight design ensures strong cross-platform adaptability and provides reliable technical support for intelligent bridge management and maintenance.https://www.mdpi.com/1424-8220/25/11/3276bridge crack detectionYOLO11lightweightefficient multiscale convolutionefficient grouped convolutionremote monitoring |
| spellingShingle | Xuwei Dong Jiashuo Yuan Jinpeng Dai Study on Lightweight Bridge Crack Detection Algorithm Based on YOLO11 Sensors bridge crack detection YOLO11 lightweight efficient multiscale convolution efficient grouped convolution remote monitoring |
| title | Study on Lightweight Bridge Crack Detection Algorithm Based on YOLO11 |
| title_full | Study on Lightweight Bridge Crack Detection Algorithm Based on YOLO11 |
| title_fullStr | Study on Lightweight Bridge Crack Detection Algorithm Based on YOLO11 |
| title_full_unstemmed | Study on Lightweight Bridge Crack Detection Algorithm Based on YOLO11 |
| title_short | Study on Lightweight Bridge Crack Detection Algorithm Based on YOLO11 |
| title_sort | study on lightweight bridge crack detection algorithm based on yolo11 |
| topic | bridge crack detection YOLO11 lightweight efficient multiscale convolution efficient grouped convolution remote monitoring |
| url | https://www.mdpi.com/1424-8220/25/11/3276 |
| work_keys_str_mv | AT xuweidong studyonlightweightbridgecrackdetectionalgorithmbasedonyolo11 AT jiashuoyuan studyonlightweightbridgecrackdetectionalgorithmbasedonyolo11 AT jinpengdai studyonlightweightbridgecrackdetectionalgorithmbasedonyolo11 |