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YOLOv11-LLR: An Enhanced Framework for Steel Surface Defect Detection in Industrial Settings

Steel surface defects in manufacturing are typically tiny, low-contrast, and boundary-ambiguous, especially under complex textures (e.g., rolling marks, crazing), poor illumination, and high noise. These characteristics cause frequent missed detections and localization errors, particularly for defec...

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Principais autores: Jin Li, Yingjian Yang, Runhua Geng, Yaohui Chang, Yuan Jiang, Kaiwen Wu, Jinhuan Lu
Format: Artigo
Sprog:Inglês
Udgivet: MDPI AG 2026-05-01
Serier:Applied Sciences
Fag:
YOLOv11
defect detection
Deformable Large Kernel Attention (DLKA)
Lightweight Group Attention (LWGA)
Re-parameterized Convolution (RepConv)
Online adgang:https://www.mdpi.com/2076-3417/16/10/4609
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