A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospects

TL;DR

This survey comprehensively reviews recent advances in industrial defect detection, emphasizing the shift from closed-set to open-set frameworks and highlighting challenges, trends, and future prospects in 2D and 3D modalities.

Contribution

It provides an in-depth analysis of both closed-set and open-set defect detection methods, charting their evolution and emphasizing the importance of open-set techniques for real-world applications.

Findings

Open-set detection reduces annotation needs and recognizes novel defects.

Deep learning has significantly improved defect detection in 2D and 3D modalities.

Emerging trends focus on practical challenges and future directions.

Abstract

Industrial defect detection is vital for upholding product quality across contemporary manufacturing systems. As the expectations for precision, automation, and scalability intensify, conventional inspection approaches are increasingly found wanting in addressing real-world demands. Notable progress in computer vision and deep learning has substantially bolstered defect detection capabilities across both 2D and 3D modalities. A significant development has been the pivot from closed-set to open-set defect detection frameworks, which diminishes the necessity for extensive defect annotations and facilitates the recognition of novel anomalies. Despite such strides, a cohesive and contemporary understanding of industrial defect detection remains elusive. Consequently, this survey delivers an in-depth analysis of both closed-set and open-set defect detection strategies within 2D and 3D modalities, charting their evolution in recent years and underscoring the rising prominence of open-set techniques. We distill critical challenges inherent in practical detection environments and illuminate emerging trends, thereby providing a current and comprehensive vista of this swiftly progressing field.