Automatic Detection and Classification of Bridge Cracks Based on Improved YOLOv5s Algorithm
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.
automatic detection of bridge crackscrack classificationdeep learningYOLOv5sat-tention mechanism
万方数据
维普
