Application of Visual Attention Network in Workpiece Surface Defect Detection

Workpiece surface defect detection is an indispensable part of modern industrial production and the effect of surface defect detection can be improved effectively by convolutional neural network.When the surface has tiny defects,the features of the defective part are easily covered by the features of other areas,thus affecting the accuracy of detection.In this paper,a three-level attention network is proposed,in which each level network consists of three convolution modules and one visual attention module.The soft attention template from the attention module is used as the weight of the feature map of the backbone network with convolution modules to enhance features of defect regions and suppresses background region features,and improves the accuracy of defect detection.Five kinds of workpiece images with obvious defects and minor defects are tested in the experiment.The experimental results show that the soft attention templates have higher weights in areas prone to defects and the accuracy of defect detection is improved from 90.9%to 98.1%by adding visual attention module.