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

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.