Two-Stage Metal Surface Defect Detection and Classification System Based on VAEGAN and Class-Embedding

Abstract

Surface defect detection is crucial in maintaining product quality across various industries. Traditional manual inspection methods are often time-consuming and subjective, which can result in inaccuracies and higher production costs. With the use of deep learning techniques, significant advancements have been made in automating the process of surface defect detection in recent years. Moreover, deep learning includes a variety of techniques, and image recognition-based deep learning is especially relevant to our field of study, which is the main focus of this research paper.

In the industrial surface defect detection field, researchers have always aimed to create a deep learning-based intelligent defect detection system that achieves near-zero defect rates while maintaining a lightweight, efficient, and cost-effective solution. However, these objectives often conflict with each other, and it is unrealistic to develop a model that can achieve all of them simultaneously. Some trade-offs must be made. If accuracy is the top priority, a large amount of defective data labeled for supervised learning is usually required. If lightweight and low cost is prioritized, a simple small model such as Auto-Encoder is usually used, along with a large number of flawless images for unsupervised learning to minimize the cost of labeling.

As mentioned before, it is very difficult to design a single model that can achieve all of them simultaneously. However, present-day studies frequently center on accomplishing those tasks using a single model and rarely address the multi-model architecture. This paper presents a Surface Defect Detection and Classification System that builds on the current state-of-the-art model in the field of surface defect detection, along with the zero-shot learning (ZSL) classifier based on VAEGAN and the Variational Auto-Encoder developed by our laboratory.

We have developed a Surface Defect Detection and Classification System that effectively integrates the aforementioned three models. It has been validated on a dataset of metal surface defects, yielding excellent results. This system not only achieves defect detection rates that comply with industrial standards and low false positive rates but also maintains characteristics such as lightweight, low latency, and low annotation cost. In addition to achieving the above goals, this system can also instantly identify and issue anomaly notifications when there are unseen anomalies, which is generally impossible to do with supervised learning anomaly detection models.