Crack image detection method based on Faster R-CNN parameter migration

The invention discloses a crack image detection method based on Faster R-CNN parameter migration. The crack image detection method comprises the following detailed steps: 1) feature extraction: inputting a picture into a ResNet-50 network to extract features; 2) feature fusion and candidate region generation: inputting the obtained feature map into a multi-task enhanced RPN model, improving the size and the size of an anchor box of the RPN model to improve the detection and recognition precision, and generating a candidate region; and 3) detection processing: sending the feature map and the candidate region to a region of interest (ROI) pool, completely connecting the feature map and the candidate region to an (FC) layer, and then respectively connecting FC layer output to a boundary regression device and an SVM classifier to obtain the category and the position of the target. The crack image detection method solves the problem that dam crack image samples are insufficient, and is suitable for detecting crack