ArticleScientific reports2025
An improved YOLOv5n algorithm for detecting surface defects in industrial components.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- MSFE-YOLO: A Steel Surface Defect Detection Algorithm Integrating Multi-Scale Frequency Domain and Defect-Aware Attention.Sensors (Basel, Switzerland) · 2026Article
- Research on lung nodule detection in X-ray plain films based on improved YOLOv12 model.Scientific reports · 2026Article
- Hierarchical depth aware YOLO for efficient metal surface defect detection.Scientific reports · 2026Article
- Composite material surface microscopic defect detection and classification combining diffusion models and zero-shot learning.Scientific reports · 2025Article
- Attention-guided hybrid learning for accurate defect classification in manufacturing environments.Scientific reports · 2025Article
- TCQI-YOLOv5: A Terminal Crimping Quality Defect Detection Network.Sensors (Basel, Switzerland) · 2025Article
- Balancing complexity and accuracy for defect detection on filters with an improved RT-DETR.Scientific reports · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Due to the small defect areas and indistinct features on industrial components, detecting surface defects with high accuracy remains challenging, often leading to false detections. To address these issues, this paper proposes an improved YOLOv5n algorithm for industrial surface defect detection. The main improvements are as follows: the DSConv-CA module in the backbone network enhances the feature extraction capability, the Gold-YOLO structure replaces the original PANet structure in the neck to improve information fusion, and the SIoU loss function is adopted to replace the regression loss, further improving detection accuracy. Experimental results demonstrate that the improved YOLOv5n algorithm achieves a mean average precision of 75.3% on the NEU-DET dataset, which is 4.3% higher than the original model.
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Registered trials
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