Gear Defect Detection Based on Improved ResNet101 Network
Aiming at the problems such as low detection accuracy,weak feature extraction ability and unsta-ble detection model,a gear defect detection method with improved ResNet101 network was proposed in this paper.Firstly,based on ResNet101 network,the cavity convolution operation was introduced,and the expan-sion coefficients of different proportions were introduced into each residual layer to realize feature extraction under different sensitivity fields of the gear image.Secondly,dense joint operation was introduced between each convolutional module to retain shallow feature information,which reduces the risk of gradient disappea-ring during model training.Finally,the gear defect samples were obtained by rotating operation,and the per-formance of the proposed method was verified by accuracy,recall rate,ROC curve,AUC and other parame-ters.The experimental results show that the improved ResNet101 can effectively detect gear defects and has higher stability performance.It can be used for real-time on-line detection of product quality in gear production.
deep learningResNet101 networkgear defectsfeature extraction
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