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作 者:王志东 陈晨阳 刘晓明 WANG Zhidong;CHEN Chenyang;LIU Xiaoming(Jiangyin Xi Neng Industry Company Limited,Jiangyin Jiangsu 214400,China;Jiangyin Power Grid Company Limited,Jiangyin Jiangsu 214400,China)
机构地区:[1]江阴市锡能实业有限公司,江苏江阴214400 [2]国网江阴市供电公司,江苏江阴214400
出 处:《图学学报》2025年第2期241-248,共8页Journal of Graphics
摘 要:随着通信线路覆盖面积的增长,传统全介质自承式光缆(ADSS)电腐蚀缺陷巡检方式存在效率低、成本高等缺点,据此提出一种基于特征自适应提取的ADSS通信光缆电腐蚀缺陷检测方法,通过对YOLOv8n模型进行针对性改进,实现对ADSS光缆电腐蚀缺陷图像的检测。首先在主干网络中引入ADown下采样模块,在下采样过程中保留更多光缆特征的细节信息。随后引入上下文特征增强模块,使算法更有针对性地学习光缆缺陷特征。最后提出一种基于特征自适应提取的C2f_DSC模块,在颈部网络中加入,利用动态蛇形卷积特性加强对光缆区域特征的提取。在ADSS光缆电腐蚀缺陷数据集上进行实验,改进算法相比基线模型YOLOv8n在mAP50精度上提高了2.5%,在mAP50∶95精度上提高了2.2%,为ADSS光缆巡检提供了一种新的有效方法。With the expansion of communication line coverage,the traditional inspection method for electrical corrosion defects in all dielectric self-supporting(ADSS)optical cables have faced issues of low efficiency and high costs.To address these issues,a detection method for electrical corrosion defects in ADSS communication cables based on adaptive feature extraction was proposed.This method achieved detection of electrical corrosion defects in ADSS cables by making targeted improvements to the YOLOv8n model.Firstly,an ADown downsampling module was introduced into the backbone network to preserve more detailed information about the cable during the downsampling process.Subsequently,a context feature enhancement module was introduced,enabling the algorithm to learn the defect features of optical cables more specifically.Finally,a C2f_DSC module based on feature adaptive extraction was proposed,utilizing the dynamic serpentine convolution feature in the neck network to enhance the extraction of cable area features.Experiments conducted on an ADSS cable electrical corrosion dataset demonstrated that compared to the baseline model YOLOv8n,the proposed algorithm achieved a 2.5%improvement in mAP50 accuracy and a 2.2%increase in mAP50∶95 accuracy,providing a new and effective method for ADSS cable inspection.
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