An improved YOLOv5n algorithm for detecting surface defects in industrial components

An improved YOLOv5n algorithm for detecting surface defects in industrial components

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 detec...

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Bibliographic Details
Main Authors: Jia-Hui Tian, Xue-Feng Feng, Feng Li, Qing-Long Xian, Zhen-Hong Jia, Jie-Liang Liu
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:Scientific Reports
Subjects:
Defect detection
Attention mechanism
Dynamic snake Convolution
SIoU loss function
Online Access:https://doi.org/10.1038/s41598-025-94109-8
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https://doi.org/10.1038/s41598-025-94109-8

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