FSSDD: Few‐shot steel defect detection based on multi‐scale semantic enhancement representation and mask category information mapping

Abstract

Abstract Steel defect detection is important for industry production as it is tied to the product quality and production efficiency. However, previous steel defect detection methods based on deep convolutional neural networks heavily rely on large‐scale data for training and tend to have poor generalization ability for a novel defect category. In this paper, a novel few‐shot steel defect detection model based on multi‐scale semantic enhancement representation and mask category information mapping is introduced, where only a few annotated samples are acquired for the novel defect category. More concretely, three main components are built: an information‐guidance enhanced multi‐head detector is proposed to improve the representation of information in meta‐feature maps, a mask category representation module is designed to enhance the category feature representation of the mask region in the support set, and a novel multi‐scale category edge loss function is designed to assist the generation of category reweighting vector. Extensive experiments on the North‐east University few‐shot steel defect data set demonstrate that the proposed method significantly outperforms the state‐of‐the‐art methods and verify its effectiveness through ablation studies.

Keywords

• computer vision
• convolutional neural nets
• object detection
• steel