RSM-YOLOv11: Lightweight Steel Surface Defect Segmentation Algorithm Research Based on YOLOv11 Improvement

RSM-YOLOv11: Lightweight Steel Surface Defect Segmentation Algorithm Research Based on YOLOv11 Improvement

Traditional segmentation algorithms fail to effectively handle complex texture backgrounds and diverse defect shapes in steel surface defect segmentation, resulting in insufficient segmentation accuracy (SA), slow speed, and false negatives. To address these issues, this paper proposes an improved Y...

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Bibliographic Details
Main Authors: Zenghai Shan, Hu Haoyan, Changjian Zhu, Shaowen Du, Hongtao Jing, Wang Haibin
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Surface defect
YOLOv11
space-to-depth convolution
re-parameterizable block
multi-scale channel attention and spatial attention module
Online Access:https://ieeexplore.ieee.org/document/11053860/
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https://ieeexplore.ieee.org/document/11053860/

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