EAD-YOLOv10: Lightweight Steel Surface Defect Detection Algorithm Research Based on YOLOv10 Improvement

EAD-YOLOv10: Lightweight Steel Surface Defect Detection Algorithm Research Based on YOLOv10 Improvement

In response to the issues of low detection accuracy (DA), slow speed, and missed detections caused by the complex texture background and diverse shapes of surface defects (SD) in steel, this paper designs an improved lightweight YOLOv10 model called EAD-YOLOv10. By incorporating an Adaptive Downsamp...

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Bibliographic Details
Main Authors: Hu Haoyan, Tong Jinwu, Wang Haibin, Lu Xinyun
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Defect detection
YOLOv10
adaptive downsampling
dynamic upsampling
C2f_EMSCP
Online Access:https://ieeexplore.ieee.org/document/10930899/
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