Author(s)

Ranning Deng ¹

Affiliation(s)

¹ School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, China

Corresponding Author

Ranning Deng

ABSTRACT

Surface defects of industrial products directly affect product quality, operational safety, and market competitiveness. Traditional manual inspection methods suffer from low efficiency, strong subjectivity, and high missed detection rates, which can hardly meet the high-precision and high-speed inspection requirements of modern industrial production. With the advantages of non-contact measurement, high automation, and stable detection results, machine vision technology has gradually become a core technical means in the field of industrial surface defect detection. This paper focuses on the surface defect detection scenarios of typical industrial materials such as metals, plastics, and glass, systematically sorting out the application logic and applicable scenarios of three core machine vision technologies: object detection, semantic segmentation, and image classification. It details the characteristics and application scopes of mainstream public datasets such as NEU-DET and MTM-Surface-Defect, and deeply analyzes the influence mechanisms of key factors such as illumination changes and material reflection on detection accuracy. Finally, centering on the real-time inspection needs of production lines, it looks forward to future development directions such as lightweight model deployment and multimodal data fusion. This paper aims to provide a comprehensive technical reference for researchers and engineers in the field of industrial surface defect detection, and promote the large-scale application and optimization upgrading of machine vision technology in industrial production.

KEYWORDS

Machine Vision; Industrial Surface Defect Detection; Object Detection; Semantic Segmentation; Datasets; Lightweight Deployment

CITE THIS PAPER

Ranning Deng, A Review of the Applications of Machine Vision in Industrial Surface Defect Detection. Journal of Artificial Intelligence Practice (2025) Vol. 8: 144-151. DOI: http://dx.doi.org/10.23977/jaip.2025.080318.

REFERENCES

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[4] Ma, Xiaolin, et al. "YOLOv7-BA: A Metal Surface Defect Detection Model Based On Dynamic Sparse Sampling And Adaptive Spatial Feature Fusion." 2024 IEEE 6th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). Vol. 6. IEEE, 2024.
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