YOLO-RD: A Road Damage Detection Method for Effective Pavement Maintenance
Road damage detection is crucial for ensuring road safety and minimizing maintenance costs. However, detecting small damage, managing complex backgrounds, and identifying irregular damage shapes remain significant challenges. To address these issues, we propose YOLO-RD, an advanced detection framewo...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1442 |
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| author | Wei Wang Xiaoru Yu Bin Jing Ziqi Tang Wei Zhang Shengyu Wang Yao Xiao Shu Li Liping Yang |
| author_facet | Wei Wang Xiaoru Yu Bin Jing Ziqi Tang Wei Zhang Shengyu Wang Yao Xiao Shu Li Liping Yang |
| author_sort | Wei Wang |
| collection | DOAJ |
| description | Road damage detection is crucial for ensuring road safety and minimizing maintenance costs. However, detecting small damage, managing complex backgrounds, and identifying irregular damage shapes remain significant challenges. To address these issues, we propose YOLO-RD, an advanced detection framework that integrates innovative modules for feature enhancement, multi-scale robustness, and detail preservation. Specifically, the Star Operation Module (SOM) improves sensitivity to small-scale damage, the Multi-dimensional Auxiliary Fusion (MAF) module strengthens robustness in complex environments, and the Wavelet Transform Convolution (WTC) enables adaptive focus on irregular shapes. On the Japanese road dataset in RDD2022, YOLO-RD achieves a detection accuracy of 25.75%, with a notable 4.93% improvement in small object detection over the baseline YOLOv8. These results demonstrate the effectiveness and practicality of YOLO-RD in addressing diverse and challenging real-world scenarios, establishing it as a robust solution for automated road condition monitoring. |
| format | Article |
| id | doaj-art-c691e851804f4c9da258f4cd4af46a2a |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c691e851804f4c9da258f4cd4af46a2a2025-08-20T02:53:22ZengMDPI AGSensors1424-82202025-02-01255144210.3390/s25051442YOLO-RD: A Road Damage Detection Method for Effective Pavement MaintenanceWei Wang0Xiaoru Yu1Bin Jing2Ziqi Tang3Wei Zhang4Shengyu Wang5Yao Xiao6Shu Li7Liping Yang8College of Computer Science and Technology, Changchun University, No. 6543, Satellite Road, Changchun 130022, ChinaCollege of Computer Science and Technology, Changchun University, No. 6543, Satellite Road, Changchun 130022, ChinaCollege of Computer Science and Technology, Changchun University, No. 6543, Satellite Road, Changchun 130022, ChinaSchool of Construction Engineering, Jilin University, No. 2699, Qianjin Street, Changchun 130012, ChinaCollege of Computer Science and Technology, Changchun University, No. 6543, Satellite Road, Changchun 130022, ChinaCollege of Computer Science and Technology, Changchun University, No. 6543, Satellite Road, Changchun 130022, ChinaCollege of Computer Science and Technology, Changchun University, No. 6543, Satellite Road, Changchun 130022, ChinaCollege of Computer Science and Technology, Changchun University, No. 6543, Satellite Road, Changchun 130022, ChinaCollege of Computer Science and Technology, Changchun University, No. 6543, Satellite Road, Changchun 130022, ChinaRoad damage detection is crucial for ensuring road safety and minimizing maintenance costs. However, detecting small damage, managing complex backgrounds, and identifying irregular damage shapes remain significant challenges. To address these issues, we propose YOLO-RD, an advanced detection framework that integrates innovative modules for feature enhancement, multi-scale robustness, and detail preservation. Specifically, the Star Operation Module (SOM) improves sensitivity to small-scale damage, the Multi-dimensional Auxiliary Fusion (MAF) module strengthens robustness in complex environments, and the Wavelet Transform Convolution (WTC) enables adaptive focus on irregular shapes. On the Japanese road dataset in RDD2022, YOLO-RD achieves a detection accuracy of 25.75%, with a notable 4.93% improvement in small object detection over the baseline YOLOv8. These results demonstrate the effectiveness and practicality of YOLO-RD in addressing diverse and challenging real-world scenarios, establishing it as a robust solution for automated road condition monitoring.https://www.mdpi.com/1424-8220/25/5/1442road damage detectionYOLO-RDstar operation modulemulti-dimensional auxiliary fusionwavelet transform convolution |
| spellingShingle | Wei Wang Xiaoru Yu Bin Jing Ziqi Tang Wei Zhang Shengyu Wang Yao Xiao Shu Li Liping Yang YOLO-RD: A Road Damage Detection Method for Effective Pavement Maintenance Sensors road damage detection YOLO-RD star operation module multi-dimensional auxiliary fusion wavelet transform convolution |
| title | YOLO-RD: A Road Damage Detection Method for Effective Pavement Maintenance |
| title_full | YOLO-RD: A Road Damage Detection Method for Effective Pavement Maintenance |
| title_fullStr | YOLO-RD: A Road Damage Detection Method for Effective Pavement Maintenance |
| title_full_unstemmed | YOLO-RD: A Road Damage Detection Method for Effective Pavement Maintenance |
| title_short | YOLO-RD: A Road Damage Detection Method for Effective Pavement Maintenance |
| title_sort | yolo rd a road damage detection method for effective pavement maintenance |
| topic | road damage detection YOLO-RD star operation module multi-dimensional auxiliary fusion wavelet transform convolution |
| url | https://www.mdpi.com/1424-8220/25/5/1442 |
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