A Review of Metal Surface Defect Detection Technologies in Industrial Applications

• 出版年: IEEE ACCESS
• 主要な著者: Qiao, Qi; Hu, Huiying; Ahmad, Azlin; Wang, Ke
• フォーマット: Review
• 言語: English
• 出版事項: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC 2025
• 主題: Computer Science; Engineering; Telecommunications
• オンライン･アクセス: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001449658300021

Surface defects, including cracks, scratches, and deformations, significantly affect the product performance and service life in industrial manufacturing. Accurate detection of metal surface defects ensures product quality and reliability. Traditional nondestructive testing (NDT) methods, such as ultrasonic and eddy current inspections, are effective but face challenges in terms of efficiency and scalability for industrial applications. This review examines the recent advancements in automated defect detection, focusing on image acquisition, image processing, and detection techniques. The study also evaluates state-of-the-art machine learning and deep learning methods used in defect detection, including the Symmetric Convolutional Network (SCN), Swin Transformer You Only Look Once (ST-YOLO), and Statistical Texture Feature Enhancement Network (STFE-Net), which achieve high accuracy in defect classification and detection. In addition, advanced three-dimensional (3D) detection methods such as photometric stereo, light-field imaging, and structured light have been explored for their potential to address challenges such as real-time detection, few-shot detection, and small-target detection. The study concludes by identifying current limitations and proposing future directions for enhancing industrial defect detection systems.