Internal Groove Defect Detection Method of Brake Master Cylinder Based on FCOS Neural Network
WANG Zhi-wei1,GUO Bin1,HU Xiao-feng1,2,LUO Zai1,DUAN Lin-mao3
1. College of Metrology and Measurement Engineering, China Jiliang University, Hangzhou,Zhejiang 310018, China
2. Zhejiang Key Laboratory of Advanced Manufacturing Technology, Hangzhou,Zhejiang 310058, China
3. Hangzhou Wolei Intelligent Technology Co. Ltd, Hangzhou,Zhejiang 310018, China
Abstract Aiming at the difficulties of complicated interference factors and low detection accuracy in the detection of groove defects in the main cylinder, a detection algorithm for groove defects in the main cylinder based on full convolution single stage neural network (FCOS) was proposed. FPN network was used for feature extraction and pixel by pixel prediction, and the predicted results were classified to realize automatic detection of groove defects. The experimental results show that the mAP values of FCOS network in detecting the sand hole, scratch and vibration pattern in the inner groove of the main cylinder are 85.2%, 87.5% and 90.1%,and the detection accuracy is 0.98, 0.89 and 0.95. Finally, the experimental results were compared with those of the Mask R-CNN network and Faster R-CNN network. FCOS network had higher accuracy, significantly shortened learning time and satisfied real-time detection requirements.
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2021, Vol. 42 
