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...

وصف كامل

التفاصيل البيبلوغرافية
الحاوية / القاعدة:IEEE Access
المؤلفون الرئيسيون: Hu Haoyan, Tong Jinwu, Wang Haibin, Lu Xinyun
التنسيق: مقال
اللغة:الإنجليزية
منشور في: IEEE 2025-01-01
الموضوعات:
Defect detection
YOLOv10
adaptive downsampling
dynamic upsampling
C2f_EMSCP
الوصول للمادة أونلاين:https://ieeexplore.ieee.org/document/10930899/

الانترنت

https://ieeexplore.ieee.org/document/10930899/

مواد مشابهة