华东交通大学学报2024，Vol.41Issue(2)：56-63,8.
基于注意力机制的YOLOv5路面裂缝检测与识别
Detection and Recognition of YOLOv5 Pavement Cracks Based on Attention Mechanism
摘要
Abstract
[Objective]Aiming at the problem of poor real-time performance and low precision of traditional pavement crack detection.[Method]This paper uses the advantages of deep learning network in target detection,and proposes an improved YOLOv5 algorithm,which is called YOLOv5s-attention in this paper,to realize the automatic detection and recognition of pavement cracks.Firstly,the collected crack images are manually labeled with LabelImg annotation software,and then the network model parameters were obtained by improving the YO-LOv5 network training.Finally,the model is used to verify and predict the cracks.In addition,F1 and mAPare used to compare the performance of the original YOLOv5s and YOLOv5s-attention models in detecting and identifying pavement cracks.[Result]The comparison between YOLOv5s and YOLOv5s-attention showed that the precision of YOLOv5s attention increased by 1.0%,F1 increased by 0.9%,and mAPincreased by 1.8%.[Conclusion]It can be seen that the network has certain practical significance in realizing the automatic recognition of road cracks.关键词
道路养护/路面裂缝/目标检测/YOLO/注意力机制/图像处理Key words
maintenance/pavement cracks/object detection/YOLO/attention mechanism/image processing分类
建筑与水利引用本文复制引用
周双喜,杨丹,潘远,丁建新,丁杨..基于注意力机制的YOLOv5路面裂缝检测与识别[J].华东交通大学学报,2024,41(2):56-63,8.基金项目
国家自然科学基金项目(51968022) （51968022）
江西省主要学科学术和技术带头人培养计划(20213BCJL22039) （20213BCJL22039）
