Defect Detection for Wear Debris Based on Few-Shot Contrastive Learning

Abstract

In industrial defect detection tasks, the low probability of occurrence of severe industrial defects under normal production conditions has brought a great challenge for data-driven deep learning models that have just a few samples. Contrastive learning based on a sample pair makes it possible to obtain a great number of training samples and learn effective features quickly. In the field of industrial defect detection, the features of some defect instances have small category variance, and the scale of some defect instances has a great diversity. We propose a few-shot object detection network based on contrastive learning and multi-scale feature fusion. Aligned contrastive loss is adopted to increase the instance-level intra-class compactness and inter-class variance, and the misalignment problem is alleviated to a certain extent. A multi-scale fusion module is designed to recognize multi-scale defects by adaptively fusing features from different resolutions with the idea of exploiting the support branch’s information. The robustness and efficiency of the proposed method were evaluated on an industrial wear debris defect dataset and the MS COCO dataset.