Multi-task Enhanced Dam Crack Image Detection Based on Faster R-CNN

Abstract

To improve the detection accuracy for multiple small targets with Raster R-CNN model, we propose a Multitask Enhanced dam crack image detection method based on Faster R-CNN (ME-Faster R-CNN) to adapt the detection of dam cracks in different lighting environments and lengths. To solve the problem of insufficient samples of dam cracks, transfer learning methods are utilized to assist network training and data enhancement. In the ME-Faster R-CNN, ResNet-50 network is firstly adopted to extract features of original images and obtain the feature map. Then, the features map is input into multi-task enhanced RPN module to generate the candidate regions through adopting the appropriate size and dimension of anchor box. At last, the features map and candidate regions are processed to detect the dam cracks. Experimental results demonstrate that ME Faster R-CNN with transfer learning can obtain 82.52% average IoU and 80.08% average precision mAP, respectively. Compared with Faster R-CNN detection method with the same parameters, the average IoU and mAP can increase 1.06% and 1.56%, respectively.