TruingDet: Towards high-quality visual automatic defect inspection for mental surface

Abstract

Visual surface defect detection, which aims to obtain the locations of defects and classify each defect into the corresponding category in a given image, is a critical task in an actual production process. Nowadays, more and more methods have made excellent progress in visual defect inspection. However, there still exist three tough challenges where these methods cannot handle well: large defect shape change, large-scale variation, and high-quality defect localization. In this paper, a Convolutional Neural Networks (CNN) based visual defect detection framework is proposed, which elegantly mitigated these three problems by introducing three well-designed components including deformable convolution module, balanced feature pyramid module and cascade head module. First, the feature maps contained with defect shape information are adaptively extracted by Resnet/ResneXt network with the deformable convolution operator. Then the balanced feature pyramid module is attached to the feature extraction module to obtain information-fused multilayer feature maps. Finally, the cascade head is applied to refine the predicted bounding box to achieve high-quality defect localization. Under the COCO evaluation metrics, our method significantly obtains 45.2 mAP with a large margin (4.9 AP) compared with Faster RCNN baseline.