Improved faster-RCNN based inspection of hydraulic cylinder internal surface defects

Xin Zhang; QinLe Zhou; ChengFeng Wu; JianQuan Pan; HuiJun Qi

doi:10.1117/12.2687001

21 July 2023 Improved faster-RCNN based inspection of hydraulic cylinder internal surface defects

Xin Zhang, QinLe Zhou, ChengFeng Wu, JianQuan Pan, HuiJun Qi

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127170U (2023) https://doi.org/10.1117/12.2687001
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

An improved Faster-RCNN defect recognition model is proposed for the recognition of hydraulic cylinder internal surface damage defects of hydraulic supports. ResNet-50 is used instead of the original feature extraction network VGG16. Meanwhile, the anchor window generated by the region suggestion network is improved using the K-means clustering algorithm, the original ROI pooling operation is replaced by RoI Align to solve the region mismatch problem caused by the quantization operation, the soft non-maximal value suppression algorithm is used for screening, and the difficult negative sample mining method to optimize the model training process to improve the generalization ability of the model. The experimental results demonstrate that the mAP of the model on the dataset is improved by 5.87% compared with the original model, reaching 81.26%.

Citation Download Citation

Xin Zhang, QinLe Zhou, ChengFeng Wu, JianQuan Pan, and HuiJun Qi "Improved faster-RCNN based inspection of hydraulic cylinder internal surface defects", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127170U (21 July 2023); https://doi.org/10.1117/12.2687001

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