[目的]针对传统路面裂缝检测实时性差、准确率低的问题.[方法]利用深度学习网络在目标检测方面的优势,提出一种改进的YOLOv5算法,称为YOLOv5s-attention,以实现路面裂缝自动化检测与识别.首先,对采集到的裂缝图片用LabelImg标注软件进行手工标记,然后通过改进YOLOv5网络训练得到网络模型参数.最后,利用所建立的模型对裂缝进行验证和预测.除此之外,采用综合评价指标(F1-measure,F1)和平均精度均值(mean average precision,mAP)这两个指标来比较原YO-LOv5s、YOLOv5s-attention模型在路面裂缝上检测与识别的性能.[结果]经YOLOv5s与YOLOv5s-attention比较发现,YO-LOv5s-attention检测准确率(Precision)提高1.0%,F1提高0.9%,mAP提高了1.8%.[结论]由此可知,该网络在实现道路裂缝自动化识别上具有一定的现实意义.
Detection and Recognition of YOLOv5 Pavement Cracks Based on Attention Mechanism
[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.
国家科技期刊平台
NSTL
万方数据
