Defect detection network based on feature fusion and parallel cascade design

Abstract

At present, the defect detection link is indispensable in industrial production, and for the problem of low accuracy of existing neural networks in steel surface defect detection, a Faster-RCNN-based steel surface defect detection network (P-RCNN) is proposed in this paper. First, a new backbone network (Resnet-P2) is formed by adding feature fusion and attention mechanism to the original Faster-RCNN backbone network Resnet50, which can make the features transfer more effectively in the backbone network; second, a parallel cascade network is introduced, which aims to train a high-quality detector without reducing the number of high-quality samples. The purpose is to train a high quality detector without reducing the number of high quality samples, which makes the detection accuracy better. The experimental results show that the P-RCNN algorithm is used for steel surface defect detection with better detection performance and the accuracy is improved by 7.6% to 83.4% compared with Faster-RCNN, which can be used as a reference for steel surface defect identification applications.