Flexi-YOLO: A lightweight method for road crack detection in complex environments.
Road crack detection is critical to global infrastructure maintenance and public safety, and complex background environments and nonlinear damage crack patterns challenge the need for real-time, efficient, and accurate detection.This paper proposes a lightweight yet robust Flexi-YOLO model based on...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0325993 |
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| author | Jiexiang Yang Renjie Tian Zexing Zhou Xingyue Tan Pingyang He |
| author_facet | Jiexiang Yang Renjie Tian Zexing Zhou Xingyue Tan Pingyang He |
| author_sort | Jiexiang Yang |
| collection | DOAJ |
| description | Road crack detection is critical to global infrastructure maintenance and public safety, and complex background environments and nonlinear damage crack patterns challenge the need for real-time, efficient, and accurate detection.This paper proposes a lightweight yet robust Flexi-YOLO model based on the YOLOv8 algorithm. We designed Wise-IoU as the model's loss function to optimize the regression accuracy of its bounding boxes and enhance robustness to low-quality samples. The DCNv-C2f module is constructed for the transformation and fusion of feature information, allowing the convolutional kernels to adapt to the complex shape characteristics of cracks dynamically. A Global Attention Module (GAM) is integrated to improve the model's perception of global information. The AKConv convolution operation is employed to adaptively adjust the size of convolutions, further enhancing local feature capturing. Additionally, a lightweight network design is implemented, establishing G-Head (Ghost-Head) as the detection head to optimize the issue of feature redundancy. Experimental results show that Flexi-YOLO achieves an accuracy increase of 2.7% over YOLOv8n, a recall rate rise of 4.7%, a mAP improvement of 5.3%, a mAP@0.5-0.95 increase of 3.9%, a decrease of 0.5 in GFLOPS, and an F1 score improvement from 0.80 to 0.84. Flexi-YOLO offers higher detection accuracy and robustness and meets the industrial demands for lightweight real-time detection and lower application costs, providing an efficient and precise solution for the automated detection of road cracks. |
| format | Article |
| id | doaj-art-1b88e18a8e4f44a88cdbf8d4f4cf5797 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-1b88e18a8e4f44a88cdbf8d4f4cf57972025-08-20T03:30:48ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01206e032599310.1371/journal.pone.0325993Flexi-YOLO: A lightweight method for road crack detection in complex environments.Jiexiang YangRenjie TianZexing ZhouXingyue TanPingyang HeRoad crack detection is critical to global infrastructure maintenance and public safety, and complex background environments and nonlinear damage crack patterns challenge the need for real-time, efficient, and accurate detection.This paper proposes a lightweight yet robust Flexi-YOLO model based on the YOLOv8 algorithm. We designed Wise-IoU as the model's loss function to optimize the regression accuracy of its bounding boxes and enhance robustness to low-quality samples. The DCNv-C2f module is constructed for the transformation and fusion of feature information, allowing the convolutional kernels to adapt to the complex shape characteristics of cracks dynamically. A Global Attention Module (GAM) is integrated to improve the model's perception of global information. The AKConv convolution operation is employed to adaptively adjust the size of convolutions, further enhancing local feature capturing. Additionally, a lightweight network design is implemented, establishing G-Head (Ghost-Head) as the detection head to optimize the issue of feature redundancy. Experimental results show that Flexi-YOLO achieves an accuracy increase of 2.7% over YOLOv8n, a recall rate rise of 4.7%, a mAP improvement of 5.3%, a mAP@0.5-0.95 increase of 3.9%, a decrease of 0.5 in GFLOPS, and an F1 score improvement from 0.80 to 0.84. Flexi-YOLO offers higher detection accuracy and robustness and meets the industrial demands for lightweight real-time detection and lower application costs, providing an efficient and precise solution for the automated detection of road cracks.https://doi.org/10.1371/journal.pone.0325993 |
| spellingShingle | Jiexiang Yang Renjie Tian Zexing Zhou Xingyue Tan Pingyang He Flexi-YOLO: A lightweight method for road crack detection in complex environments. PLoS ONE |
| title | Flexi-YOLO: A lightweight method for road crack detection in complex environments. |
| title_full | Flexi-YOLO: A lightweight method for road crack detection in complex environments. |
| title_fullStr | Flexi-YOLO: A lightweight method for road crack detection in complex environments. |
| title_full_unstemmed | Flexi-YOLO: A lightweight method for road crack detection in complex environments. |
| title_short | Flexi-YOLO: A lightweight method for road crack detection in complex environments. |
| title_sort | flexi yolo a lightweight method for road crack detection in complex environments |
| url | https://doi.org/10.1371/journal.pone.0325993 |
| work_keys_str_mv | AT jiexiangyang flexiyoloalightweightmethodforroadcrackdetectionincomplexenvironments AT renjietian flexiyoloalightweightmethodforroadcrackdetectionincomplexenvironments AT zexingzhou flexiyoloalightweightmethodforroadcrackdetectionincomplexenvironments AT xingyuetan flexiyoloalightweightmethodforroadcrackdetectionincomplexenvironments AT pingyanghe flexiyoloalightweightmethodforroadcrackdetectionincomplexenvironments |