Research on YOLOv5-Ganomaly Joint Algorithm for High-Strength Bolt Detection
High-strength bolt loosening detection of bridges faces problems such as heavy workload,small targets,many anomalies,and difficult collection.Therefore,this paper proposed a semi-supervised deep learning model,which could obtain the bolt loosening detection model even with a small number of negative samples and solve the problem of unbalanced model training samples.The accuracy of the YOLOv5-CT model for bolt target detection reached 98.33%.By preprocessing bolt data,the reconstruction ability of bolt images by the Ganomaly model was improved.When the hidden space vector value was 100,the model had the highest SAUC and the best discriminant performance.In the model test stage,the threshold of abnormal fraction was set to 0.295,and the accuracy of the calculation model for abnormal loosening detection of high-strength bolts could reach more than 85%.As a result,the automatic identification and detection of bolts were realized.
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