An Automatic Surface Defect Detection Method with Residual Attention Network

Abstract

Automatic defect detection is of great significance to the production process of modern industries, which will affect the product appearance and quality. And a great number of economic loss may be caused due to the defects in the industrial production process. Traditional manual defect inspection method is labor-intensive and time-consuming with subjective factors, and the detection performance is random and uncertain. Machine learning has shown a good detection ability on small-scale samples, but the detect detection task against with poor contrast, weak texture, etc., will affect the effective feature representation. To address the detection task of steel surface defects, combined with the strong context extraction ability of deep learning, an accurate deep defect classification network is proposed in the paper to provide an end-to-end detection scheme. Fused with the residual network (Resnet50) and spatial attention block, a residual attention network is proposed for effective feature representation, which could make the classification network better focus on the defect areas. Meanwhile, due to the scale information change among different defects, a multi-scale context fusion (MCF) block is proposed for effective multi-scale feature extraction, which is conducive for multi-scale object detection. Experimental results on public defect data set show that the proposed defect detection network could acquire a superior classification performance compared with some typical classification networks.