CRD: Collaborative Representation Distance for Practical Anomaly Detection

TL;DR

This paper introduces CRD, a collaborative representation distance method for defect detection that significantly reduces computational complexity and memory usage, enabling efficient edge deployment with minimal performance loss.

Contribution

It proposes a novel distance calculation approach using collaborative representation models that is computationally efficient and suitable for edge environments.

Findings

Achieves hundreds of times faster computation compared to state-of-the-art methods.

Reduces memory overhead significantly.

Maintains competitive defect detection performance.

Abstract

Visual defect detection plays an important role in intelligent industry. Patch based methods consider visual images as a collection of image patches according to positions, which have stronger discriminative ability for small defects in products, e.g. scratches on pills. However, the nearest neighbor search for the query image and the stored patches will occupy $O(n)$ complexity in terms of time and space requirements, posing strict challenges for deployment in edge environments. In this paper, we propose an alternative approach to the distance calculation of image patches via collaborative representation models. Starting from the nearest neighbor distance with $L_0$ constraint, we relax the constraint to $L_2$ constraint and solve the distance quickly in close-formed without actually accessing the original stored collection of image patches. Furthermore, we point out that the main computational burden of this close-formed solution can be pre-computed by high-performance server before deployment. Consequently, the distance calculation on edge devices only requires a simple matrix multiplication, which is extremely lightweight and GPU-friendly. Performance on real industrial scenarios demonstrates that compared to the existing state-of-the-art methods, this distance achieves several hundred times improvement in computational efficiency with slight performance drop, while greatly reducing memory overhead.