A Survey on Foundation-Model-Based Industrial Defect Detection

TL;DR

This survey reviews foundation-model-based industrial defect detection, highlighting their advantages in few-shot and zero-shot learning, while discussing challenges like increased complexity and slower inference compared to traditional methods.

Contribution

It systematically compares foundation models with non-foundation models in industrial defect detection, analyzing their structures, performance, and future research directions.

Findings

FM methods excel in few-shot and zero-shot learning scenarios.

Traditional methods are faster but less adaptable to new defect types.

Lightweight FM approaches are emerging to balance accuracy and efficiency.

Abstract

As industrial products become abundant and sophisticated, visual industrial defect detection receives much attention, including two-dimensional and three-dimensional visual feature modeling. Traditional methods use statistical analysis, abnormal data synthesis modeling, and generation-based models to separate product defect features and complete defect detection. Recently, the emergence of foundation models has brought visual and textual semantic prior knowledge. Many methods are based on foundation models (FM) to improve the accuracy of detection, but at the same time, increase model complexity and slow down inference speed. Some FM-based methods have begun to explore lightweight modeling ways, which have gradually attracted attention and deserve to be systematically analyzed. In this paper, we conduct a systematic survey with comparisons and discussions of foundation model methods from different aspects and briefly review non-foundation model (NFM) methods recently published. Furthermore, we discuss the differences between FM and NFM methods from training objectives, model structure and scale, model performance, and potential directions for future exploration. Through comparison, we find FM methods are more suitable for few-shot and zero-shot learning, which are more in line with actual industrial application scenarios and worthy of in-depth research.