Crack detection based on attention mechanism with YOLOv5
Abstract In order to reduce the manual workload and reduce the maintenance cost, it is particularly important to realize automatic detection of cracks. Aiming at the problems of poor real‐time performance and low precision of traditional pavement crack detection, a crack detection method based on im...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.12899 |
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| _version_ | 1832576621752614912 |
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| author | Min‐Li Lan Dan Yang Shuang‐Xi Zhou Yang Ding |
| author_facet | Min‐Li Lan Dan Yang Shuang‐Xi Zhou Yang Ding |
| author_sort | Min‐Li Lan |
| collection | DOAJ |
| description | Abstract In order to reduce the manual workload and reduce the maintenance cost, it is particularly important to realize automatic detection of cracks. Aiming at the problems of poor real‐time performance and low precision of traditional pavement crack detection, a crack detection method based on improved YOLOv5 one‐step target detection algorithm of convolutional neural network is proposed by using the advantages of depth learning network in target detection. The images were manually marked with LabelImg annotation software, and then the network model parameters were obtained through improving the YOLOv5 network training. Finally, the cracks are verified and detected by the established model. In addition, the precision and speed of crack detection using YOLOv3, YOLOv5s, and YOLOv5s‐attention models are compared by using Precision, Recall, and F1. After comparison, it is found that the detection precision of YOLOv5s‐attention is improved by 1.0%, F1 by 0.9%, and mAP@.5 by 1.8%. |
| format | Article |
| id | doaj-art-6d05b62b3061442eb036ad4e211c9e18 |
| institution | Kabale University |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-6d05b62b3061442eb036ad4e211c9e182025-01-31T00:22:48ZengWileyEngineering Reports2577-81962025-01-0171n/an/a10.1002/eng2.12899Crack detection based on attention mechanism with YOLOv5Min‐Li Lan0Dan Yang1Shuang‐Xi Zhou2Yang Ding3Fujian Chuanzheng Communications College Fuzhou ChinaSchool of Civil Engineering and Architecture East China Jiaotong University Nanchang ChinaSchool of Civil Engineering and Architecture East China Jiaotong University Nanchang ChinaDepartment of Civil Engineering Hangzhou City University Hangzhou ChinaAbstract In order to reduce the manual workload and reduce the maintenance cost, it is particularly important to realize automatic detection of cracks. Aiming at the problems of poor real‐time performance and low precision of traditional pavement crack detection, a crack detection method based on improved YOLOv5 one‐step target detection algorithm of convolutional neural network is proposed by using the advantages of depth learning network in target detection. The images were manually marked with LabelImg annotation software, and then the network model parameters were obtained through improving the YOLOv5 network training. Finally, the cracks are verified and detected by the established model. In addition, the precision and speed of crack detection using YOLOv3, YOLOv5s, and YOLOv5s‐attention models are compared by using Precision, Recall, and F1. After comparison, it is found that the detection precision of YOLOv5s‐attention is improved by 1.0%, F1 by 0.9%, and mAP@.5 by 1.8%.https://doi.org/10.1002/eng2.12899attention mechanismimage processingobject detectioncracks identificationYOLO v5 |
| spellingShingle | Min‐Li Lan Dan Yang Shuang‐Xi Zhou Yang Ding Crack detection based on attention mechanism with YOLOv5 Engineering Reports attention mechanism image processing object detection cracks identification YOLO v5 |
| title | Crack detection based on attention mechanism with YOLOv5 |
| title_full | Crack detection based on attention mechanism with YOLOv5 |
| title_fullStr | Crack detection based on attention mechanism with YOLOv5 |
| title_full_unstemmed | Crack detection based on attention mechanism with YOLOv5 |
| title_short | Crack detection based on attention mechanism with YOLOv5 |
| title_sort | crack detection based on attention mechanism with yolov5 |
| topic | attention mechanism image processing object detection cracks identification YOLO v5 |
| url | https://doi.org/10.1002/eng2.12899 |
| work_keys_str_mv | AT minlilan crackdetectionbasedonattentionmechanismwithyolov5 AT danyang crackdetectionbasedonattentionmechanismwithyolov5 AT shuangxizhou crackdetectionbasedonattentionmechanismwithyolov5 AT yangding crackdetectionbasedonattentionmechanismwithyolov5 |