A deep learning-based automatic crack detection and classification method is proposed to overcome the shortcomings and limitations of traditional manual crack detection methods,which are time-consuming,labor-intensive,and of limited use.The YOLOv5s algorithm is used as the founda-tion,and two different attention mechanisms,SENet and Coordinate Attention,are introduced.Within a large amount of data,the high-value information is quickly filtered out by these mechanisms,thereby improving the efficiency of the YOLOv5s model in crack detection and classification.The de-tection accuracy of original YOLOv5s model is 89.2%on 1 500 images containing four types of cracks.After introducing the attention mechanisms,the accuracy of the model improved by 5.7%and 7.1%respectively,reaching 94.9%and 96.3%.The results indicate that the improved YOLOv5s al-gorithm can achieve automatic detection and classification of bridge cracks and has broad application prospects in practical bridge performance testing.
关键词
桥梁裂缝自动检测/裂缝分类/深度学习/YOLOv5s/注意力机制
Key words
automatic detection of bridge cracks/crack classification/deep learning/YOLOv5s/at-tention mechanism
维普
万方数据