A Lightweight Reconstruction Network for Surface Defect Inspection

TL;DR

This paper introduces a lightweight unsupervised surface defect detection method using a convolutional autoencoder trained only on defect-free samples, effectively identifying irregular surface defects with high robustness and accuracy.

Contribution

It proposes a novel lightweight reconstruction network with a combined loss function for unsupervised defect detection, addressing scarcity of defect samples and irregular defect textures.

Findings

High detection accuracy on multiple defect datasets

Strong robustness against various defect types

Effective localization of surface defects

Abstract

Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained defect-free sample data. The network includes two parts: image reconstruction and surface defect area detection. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. Only a small number of normal samples are used for training so that the reconstruction network can be A defect-free reconstructed image is generated. A function combining structural loss and $\mathit{L}1$ loss is proposed as the loss function of the reconstruction network to solve the problem of poor detection of irregular texture surface defects. Further, the residual of the reconstructed image and the image to be tested is used as the possible region of the defect, and conventional image operations can realize the location of the fault. The unsupervised defect detection algorithm of the proposed reconstruction network is used on multiple defect image sample sets. Compared with other similar algorithms, the results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.