Method for Detecting Surface Defects of Engine Parts Based on Faster R-CNN

To cope with the low accuracy and inefficiency problems in the detection of engine parts surface defects by manual and traditional automated methods,a deep learning based method is proposed in this work.The Faster R-CNN deep learning algorithm is used as the basis algorithm,and the clustering theory is adopted to determine the anchor scheme.By comparing the influence on the detection results of the two clustering algorithms:k-meansII and CURE in the anchor generation process,a Faster R-CNN based on clustering algorithm is proposed,and multi-level ROI pooling layer structure is adopted to reduce the deviation caused by rounding in ROI pooling process.In addition,by properly designing the defect image data acquisition scheme,three defect part data sets are constructed.The experimental results show that our proposed method can improve the mean average precision mAP of defect detection from 54.7%to 97.9%compared with the original algorithm,and the detection speed can reach as high as 4.9 fps at the fastest,which can satisfactorily meet the production requirements of intelligent manufacturing.