A Review of the Applications of Machine Vision in Industrial Surface Defect Detection
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".