Surface Defect Detection Model for Vertical Shaft Guide Rails Based on MELE-YOLOv11n
PENG Lipeng
ZHAO Baiting
Abstract：To address the low detection accuracy,difficulty in recognition,and large parameter size of models for surface defect detection in vertical shaft guide rails,a multi-enhanced lightweight efficient-you only look once version 11 nano(MELE-YOLOv11n)model was proposed.Firstly,the efficient channel attention dual-stream(ECA-DS)and the depthwise separable convolution-efficient multi-scale attention(DWEMA)modules were designed to enhance the model's adaptability to target defect features in complex mine environments and improve detection capability.Secondly,the convolutional three-scale kernel-adaptive dual-path-omni-kernel(C3K2-OK)module was introduced to capture feature map information at different scales,alleviating information loss.Finally,to address the high computational load of the original detection head,the lightweight shared detail-enhanced convolutional detection head(Detect-LSDECD)module was developed,where shared strategies were combined with detail-enhanced convolution to enhance lightweight performance.The results demonstrated that compared to the YOLOv11n model,the mean average precision of MELE-YOLOv11n model on vertical shaft guide rails surface defect dataset increased by 2.5%,and the number of parameters reduced by 0.3×106.The MELE-YOLOv11n model met the balance between accuracy and lightweight requirements,providing strong technical support for the automated detection of surface defects in vertical shaft guide rails.
Keywords：surface defectYOLOv11nshared convolutionobject detectionlightweightattention mechanism
Publication Date：2025-09-20
Online Publishing Date：2025-09-24(First online date of this platform, not the publication date of the document)
Pages：6( 376-381 )