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Related Experiment Video

Updated: May 28, 2026

Characterization of Surface Modifications by White Light Interferometry: Applications in Ion Sputtering, Laser Ablation, and Tribology Experiments

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An improved YOLOv5n algorithm for detecting surface defects in industrial components.

Jia-Hui Tian^1,2, Xue-Feng Feng³, Feng Li³

¹College of Computer Science and Technology, Xinjiang University, Urumqi, 830046, China.

Scientific Reports

|March 22, 2025

Summary

This summary is machine-generated.

This study introduces an enhanced YOLOv5n algorithm for industrial surface defect detection, improving accuracy by optimizing feature extraction and information fusion. The upgraded model achieved a 4.3% higher mean average precision on the NEU-DET dataset.

Keywords:

Attention mechanism Defect detection Dynamic snake Convolution SIoU loss function

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Area of Science:

Computer Vision
Machine Learning
Industrial Automation

Background:

Accurate industrial surface defect detection is critical but challenging due to small defect sizes and indistinct features.
Existing methods often suffer from false detections, impacting quality control and manufacturing efficiency.

Purpose of the Study:

To propose an improved YOLOv5n algorithm for enhanced industrial surface defect detection.
To address the limitations of current methods in accurately identifying subtle surface flaws.

Main Methods:

Implemented a DSConv-CA module in the backbone for superior feature extraction.
Integrated the Gold-YOLO structure in the neck to refine information fusion.
Utilized the SIoU loss function to improve bounding box regression accuracy.

Main Results:

The improved YOLOv5n algorithm achieved a mean average precision (mAP) of 75.3% on the NEU-DET dataset.
Demonstrated a 4.3% increase in mAP compared to the original YOLOv5n model.
Showcased enhanced capability in detecting small and indistinct industrial surface defects.

Conclusions:

The proposed modifications significantly boost the performance of YOLOv5n for industrial surface defect detection.
The integration of DSConv-CA, Gold-YOLO, and SIoU loss offers a robust solution for high-accuracy defect identification.
This advanced algorithm contributes to more reliable quality control in industrial manufacturing.