Automatic Visual Inspection of Rare Defects: A Framework based on GP-WGAN and Enhanced Faster R-CNN

TL;DR

This paper introduces a two-stage framework for automatic visual inspection that synthesizes new defect samples using GP-WGAN and enhances defect detection with an improved Faster R-CNN architecture, improving accuracy on imbalanced datasets.

Contribution

The paper presents a novel fault diagnosis framework combining data augmentation with GP-WGAN and an enhanced Faster R-CNN for improved defect detection in industrial AVI systems.

Findings

Outperforms existing methods on multi-class defect datasets.

Effective augmentation reduces class imbalance issues.

Improved detection accuracy demonstrated across various imbalance levels.

Abstract

A current trend in industries such as semiconductors and foundry is to shift their visual inspection processes to Automatic Visual Inspection (AVI) systems, to reduce their costs, mistakes, and dependency on human experts. This paper proposes a two-staged fault diagnosis framework for AVI systems. In the first stage, a generation model is designed to synthesize new samples based on real samples. The proposed augmentation algorithm extracts objects from the real samples and blends them randomly, to generate new samples and enhance the performance of the image processor. In the second stage, an improved deep learning architecture based on Faster R-CNN, Feature Pyramid Network (FPN), and a Residual Network is proposed to perform object detection on the enhanced dataset. The performance of the algorithm is validated and evaluated on two multi-class datasets. The experimental results performed over a range of imbalance severities demonstrate the superiority of the proposed framework compared to other solutions.