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Open Access

REVIEW

A Survey of Surface Defect Detection in Machine Vision: Addressing Core Challenges, Methodologies, and Dataset Analysis

Langyue Zhao1,2, Yubin Yuan3,*, Yiquan Wu2,*
1 College of Computer Science, Weinan Normal University, Weinan, China
2 College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China
3 College of Information Engineering, Yangzhou University, Yangzhou, China
* Corresponding Author: Yubin Yuan. Email: email; Yiquan Wu. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.080232

Received 05 February 2026; Accepted 28 April 2026; Published online 19 May 2026

Abstract

This paper presents a systematic survey of machine vision-based surface defect detection technologies, focusing on five core challenges in the field: interference from complex backgrounds, small object detection, class imbalance, dynamic scene modeling, and cross-scenario generalization. It reviews key technical approaches corresponding to these challenges over the past five years. Furthermore, a dataset characterization analysis framework is established around these challenges, summarizing and comparing the characteristics of over 40 publicly available datasets across more than ten scenarios, including PCB, photovoltaic, metal, and pavement surfaces. Quantitative selection metrics (such as the small target coefficient and texture complexity) are proposed for challenges like small target detection and complex backgrounds, offering a methodological guide for aligning research questions with benchmark data. Finally, the paper summarizes current limitations and provides an outlook on new paradigms driven by large-scale models and the construction of high-quality benchmark datasets, aiming to offer valuable references for both research and engineering practices in this field.

Keywords

Surface defect detection; machine vision; complex scenarios; dataset survey; industrial vision

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