GSBYOLO: A lightweight Multi-Scale fusion network for road crack detection in complex environments
Abstract Timely detection and regular maintenance of road cracks are critical for road and traffic safety. However, existing detection methods face challenges such as varying target scales, large model parameters, and poor adaptability to complex backgrounds. To address these issues, this study prop...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-11717-0 |
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| author | Yuhao Wang Heran Zhu Yirong Wang Jianping Liu Jun Xie Bi Zhao Siyue Zhao |
| author_facet | Yuhao Wang Heran Zhu Yirong Wang Jianping Liu Jun Xie Bi Zhao Siyue Zhao |
| author_sort | Yuhao Wang |
| collection | DOAJ |
| description | Abstract Timely detection and regular maintenance of road cracks are critical for road and traffic safety. However, existing detection methods face challenges such as varying target scales, large model parameters, and poor adaptability to complex backgrounds. To address these issues, this study proposes an enhanced GSB-YOLO model. Inspired by the concepts of linear transformation and long-range attention mechanisms, a lightweight network structure was designed to reduce model parameters in the backbone network, thereby improving detection efficiency. Additionally, a novel SMC2f module was introduced in the neck structure, which calculates the “energy” of each neuron in the feature map, evaluates its contribution to the detection task, and dynamically assigns weighted coefficients. This method enhances the model’s detection robustness in complex backgrounds and effectively addresses the issue of insufficient emphasis on positive samples. Furthermore, through the optimization of the Path Aggregation Network (PAN) and the Bidirectional Feature Pyramid Network (BiFPN), efficient multi-scale feature fusion is achieved, further strengthening the model’s capacity to represent crack features at various scales. Experimental results indicate that the proposed GSB-YOLO model improves the mean average precision (mAP) in road crack detection tasks by 3.2%, demonstrating its significant application value in road crack detection and traffic safety assurance. |
| format | Article |
| id | doaj-art-59dfa280161044c2a7891c7a2677cecc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-59dfa280161044c2a7891c7a2677cecc2025-08-20T04:03:03ZengNature PortfolioScientific Reports2045-23222025-07-0115111410.1038/s41598-025-11717-0GSBYOLO: A lightweight Multi-Scale fusion network for road crack detection in complex environmentsYuhao Wang0Heran Zhu1Yirong Wang2Jianping Liu3Jun Xie4Bi Zhao5Siyue Zhao6College of Water Conservancy and Hydropower, Sichuan Agricultural UniversityYunnan Tropical and Subtropical Animal Virus Disease Laboratory, Yunnan Academy of Animal Husbandry and Veterinary SciencesCollege of Water Conservancy and Hydropower, Sichuan Agricultural UniversityCollege of Water Conservancy and Hydropower, Sichuan Agricultural UniversityCollege of Water Conservancy and Hydropower, Sichuan Agricultural UniversityTea Research Institute, Yunnan Key Laboratory of Tea Science, Yunnan Academy of Agricultural SciencesCollege of Water Conservancy and Hydropower, Sichuan Agricultural UniversityAbstract Timely detection and regular maintenance of road cracks are critical for road and traffic safety. However, existing detection methods face challenges such as varying target scales, large model parameters, and poor adaptability to complex backgrounds. To address these issues, this study proposes an enhanced GSB-YOLO model. Inspired by the concepts of linear transformation and long-range attention mechanisms, a lightweight network structure was designed to reduce model parameters in the backbone network, thereby improving detection efficiency. Additionally, a novel SMC2f module was introduced in the neck structure, which calculates the “energy” of each neuron in the feature map, evaluates its contribution to the detection task, and dynamically assigns weighted coefficients. This method enhances the model’s detection robustness in complex backgrounds and effectively addresses the issue of insufficient emphasis on positive samples. Furthermore, through the optimization of the Path Aggregation Network (PAN) and the Bidirectional Feature Pyramid Network (BiFPN), efficient multi-scale feature fusion is achieved, further strengthening the model’s capacity to represent crack features at various scales. Experimental results indicate that the proposed GSB-YOLO model improves the mean average precision (mAP) in road crack detection tasks by 3.2%, demonstrating its significant application value in road crack detection and traffic safety assurance.https://doi.org/10.1038/s41598-025-11717-0Road crack detectionGSB-YOLOSMC2f moduleMulti-scale feature fusionYOLOv8n |
| spellingShingle | Yuhao Wang Heran Zhu Yirong Wang Jianping Liu Jun Xie Bi Zhao Siyue Zhao GSBYOLO: A lightweight Multi-Scale fusion network for road crack detection in complex environments Scientific Reports Road crack detection GSB-YOLO SMC2f module Multi-scale feature fusion YOLOv8n |
| title | GSBYOLO: A lightweight Multi-Scale fusion network for road crack detection in complex environments |
| title_full | GSBYOLO: A lightweight Multi-Scale fusion network for road crack detection in complex environments |
| title_fullStr | GSBYOLO: A lightweight Multi-Scale fusion network for road crack detection in complex environments |
| title_full_unstemmed | GSBYOLO: A lightweight Multi-Scale fusion network for road crack detection in complex environments |
| title_short | GSBYOLO: A lightweight Multi-Scale fusion network for road crack detection in complex environments |
| title_sort | gsbyolo a lightweight multi scale fusion network for road crack detection in complex environments |
| topic | Road crack detection GSB-YOLO SMC2f module Multi-scale feature fusion YOLOv8n |
| url | https://doi.org/10.1038/s41598-025-11717-0 |
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