SCL-VI: Self-supervised Context Learning for Visual Inspection of Industrial Defects

TL;DR

This paper introduces a self-supervised learning method for industrial defect detection that effectively captures rich semantic features and distinguishes normal from abnormal surfaces, achieving state-of-the-art results.

Contribution

A novel self-supervised algorithm using jigsaw puzzles and affinity augmentation to improve defect detection in industrial images.

Findings

Achieves 95.8% detection accuracy on MVTec AD dataset.

Achieves 96.8% segmentation accuracy on MVTec AD dataset.

Establishes new state-of-the-art benchmarks for texture and object defect detection.

Abstract

The unsupervised visual inspection of defects in industrial products poses a significant challenge due to substantial variations in product surfaces. Current unsupervised models struggle to strike a balance between detecting texture and object defects, lacking the capacity to discern latent representations and intricate features. In this paper, we present a novel self-supervised learning algorithm designed to derive an optimal encoder by tackling the renowned jigsaw puzzle. Our approach involves dividing the target image into nine patches, tasking the encoder with predicting the relative position relationships between any two patches to extract rich semantics. Subsequently, we introduce an affinity-augmentation method to accentuate differences between normal and abnormal latent representations. Leveraging the classic support vector data description algorithm yields final detection results. Experimental outcomes demonstrate that our proposed method achieves outstanding detection and segmentation performance on the widely used MVTec AD dataset, with rates of 95.8% and 96.8%, respectively, establishing a state-of-the-art benchmark for both texture and object defects. Comprehensive experimentation underscores the effectiveness of our approach in diverse industrial applications.