A Novel Weakly Supervised Ensemble Learning Framework for Automated Pixel-Wise Industry Anomaly Detection

Abstract

Automatic anomaly detection has always been a very important and challenging subject, which strongly affects product quality. This paper, proposes a novel weakly supervised ensemble learning based framework for automated pixel-wise anomaly detection, which enables the model to be trained using only a small quantity of labelled samples, instead of hundreds or thousands or more, meanwhile it maintains excellent performance in practical applications. Specifically, first, synthesized defects with random shapes, texture and brightness are injected into fault-free samples for preliminary training. Then homographic adaptation is introduced, and Siamese homography method is employed to further improve the training performance by limiting the repeatability and consistency of results in different Siamese network channels. While in model inference module, a multi-modal boosting method with adaptive weights is utilized instead of prediction on purely original sample images. Experiments are performed on several typical open industry anomaly detection datasets, and experimental results show that our method achieves better performance when compared with other classic superior methods. Typically, the segmentation metric mIOU can reach 85.79%, 78.38% and 76.65% on these datasets, specifically.