摘要
针对煤矿钢丝绳表面缺陷检测中存在的小目标尺度差异大、复杂背景干扰强等问题,提出一种基于改进YOLOv11的深度学习检测算法。首先,设计感受野注意力特征提取模块C3k2_RFAConv,通过动态调整卷积核权重增强复杂纹理下的特征提取能力;其次,在特征融合层引入可变形大核注意力机制D-LKA,结合大感受野与可变形卷积的优势,精准聚焦缺陷区域;此外,采用DySample上采样优化以抑制背景噪声干扰,减少小目标特征丢失;最后,提出Inner-WIoU损失函数优化边界框回归,提升不规则缺陷的定位精度。实验结果表明,改进算法在准确率、召回率和平均精度上分别达到83.2%、78.1%和82.1%,较基准模型YOLOv11提升3.1%、4.6%和2.6%,且优于Faster-RCNN、YOLOv8等对比模型,此外,通过可视化分析证明改进后的算法漏检率降低,可为矿用钢丝绳安全隐患的实时监测提供有效的技术方案。
Addressing the issues of significant scale differences in small targets and strong interference from complex backgrounds in the detection of surface defects on coal mine steel wire ropes,a deep learning detection algorithm based on an improved YOLOv11 is proposed.Firstly,a receptive field attention feature extraction module,C3k2_RFAConv,is designed to enhance feature extraction capabilities under complex textures by dynamically adjusting convolution kernel weights.Secondly,a deformable large kernel attention mechanism,D-LKA,is introduced at the feature fusion layer,combining the advantages of large receptive fields and deformable convolutions to precisely focus on defect areas.Additionally,DySample upsampling optimization is adopted to suppress background noise interference and reduce the loss of small target features.Finally,an Inner-WIoU loss function is proposed to optimize bounding box regression and improve the localization accuracy of irregular defects.Experimental results show that the improved algorithm achieves an accuracy rate of 83.2%,a recall rate of 78.1%,and an average precision of 82.1%,which are 3.1%,4.6% and 2.6%higher than those of the benchmark model YOLOv11,respectively.It also outperforms comparative models such as Faster-RCNN and YOLOv8.In addition,visual analysis proves that the improved algorithm has a reduced missed detection rate,providing an effective technical solution for real-time monitoring of potential safety hazards in mining steel wire ropes.
作者
郭旭鹏
董立红
秦昳
Guo Xupeng;Dong Lihong;Qin Yi(School of Artificial Intelligence and Computer Science,Xi′an University of Science and Technology,Xi′an 710054,China)
出处
《电子测量技术》
2026年第5期63-76,共14页
Electronic Measurement Technology
基金
国家自然科学基金青年项目(62303375)资助。
