Surface Defect Detection of Industrial Parts Based on YOLOv5
Open accessAbstract
Industrial product quality inspection, a crucial procedure in industrial production, is crucial in assuring product yield. Product safety and quality inspections on industrial assembly lines are predominantly manual, and there is currently a dearth of safe and dependable inspection techniques. An improved surface defect detection approach based on YOLOv5 is proposed for the problem of surface flaws in industrial components in order to improve the quality detection effect of industrial production parts. To improve the effect of dense object detection, the image features are extracted by the convolutional network and enhanced by coordinate attention. BiFPN is utilized to fuse multi-scale features in order to lower the rate of missed detection and false detection for small target samples. The detectors from the Transformer structure are added to the complex problem of fine-grained detection to improve the predictability of challenging occurrences. According to the experimental findings, on the dataset for industrial parts defects, the proposed network increases the recall of the original algorithm in abnormal classes by 5.3%, reaching 91.6%. Its inference speed can approach 95FPS, indicating an improved real-time detection performance.
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YOLOv5 combined with transformers detected small-scale objects and defects using a bidirectional feature pyramid network, achieving an mAP of 75.2% on grayscale imagery of surface defects.
“Combined YOLO with transformers to detect small-scale objects and defects. Proposed a bidirectional feature pyramid network, achieving an mAP of 75.2%.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [126]
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Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383 · see citing works
- Authors
- Le, H. F., Zhang, L. J., & Liu, Y. X.
- Affiliations
- Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China
- College of Urban Rail Transit and Logistics, Beijing Union University, Beijing, China
- Venue
- IEEE Access
- Published
- 2022
- Volume
- 10
- Pages
- 130784-130794
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4313127358
- Cited by
- 42
- References
- 10
- Citations by year
- 2026: 9 · 2025: 16 · 2024: 12 · 2023: 5
- FWCI
- 3.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Science and Technology Program of Beijing Municipal Education Commission (KM201911417007)
- key Program of Beijing Union University for Educational Reform (JY2021Z002)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- surface defect detection, industrial components, YOLOv5, object detection, coordinate attention, BiFPN, multi-scale feature fusion, transformer models, small object detection, quality inspection, real-time detection
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