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Oil seal defect detection method based on deep learning
Published:2023-03-23 author:XIA Gui-fang, YU Zheng-lin. Browse: 112 Check PDF documents
Oil seal defect detection method based on deep learning

XIA Gui-fang, YU Zheng-lin

(School of Mechanical and Electrical Engineering, Changchun University of 
Science and Technology, Changchun 130022, China)

Abstract: Aiming at the problems of small size of defects in the surface quality of automobile oil seals, low efficiency of manual detection, high rate of missed detection and false detection, and high cost, an optical detection method for automobile oil seal defects based on the improved deep learning Faster region with convolutional neural network(R-CNN) algorithm was proposed. Firstly, the oil seal defect detection system was constructed, the oil seal defect images were collected, and the data set was made after preprocessing such as amplification and labeling. Then, the problem of low recognition accuracy caused by the small size of the oil seal defect was studied, and the Faster R-CNN network was designed based on the feature pyramid network(FPN)+ResNet50 framework for feature multi-scale fusion and improvement. Finally, the oil seal defect detection experiment was carried out by using the pre-training parameters to send into the improved Faster R-CNN network model and deeply training the oil seal defect data set. The research result indicates, it can be seen that the comprehensive performance of the proposed model is better than the inherent Faster R-CNN network model, and the detection accuracy of scratches, burrs and dents reaches 0.96, 0.95 and 0.97, respectively. The recall rate reaches 0.89, 0.88 and 0.91, and the mean average precision(mAP) can reach 85.5%, which is 1.4% higher than the model before improvement, and the recognition speed can reach 16 fps, which is higher than the oil seal production speed, so it can meet the detection requirements of oil seal defects.

Key words:  mechanical seal; main lip problem; seal failure; Faster RCNN network model; deep training; feature multi-scale fusion

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