首页|基于三维激光点云数据的电力电缆绝缘缺陷识别

基于三维激光点云数据的电力电缆绝缘缺陷识别

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针对电力电缆绝缘缺陷识别精准度较低的问题,提出一种基于三维激光点云数据的电力电缆绝缘缺陷识别方法.利用剪裁法增强原始三维激光点云数据,通过区域生长法与最小二乘法完整获取电缆绝缘材料的三维结构面信息.借助Canny边缘检测方法求解电缆绝缘表面缺陷与内部缺陷边缘信息,自动识别出电力电缆绝缘的缺陷位置及缺陷类别.结果表明,所提方法可以精准识别电缆绝缘表面的划痕缺陷、电缆外屏蔽表面起泡和孔洞缺陷,识别耗时短,鲁棒性较优,具有较高实际应用价值.
Insulation defect identification based on 3D laser point cloud data of power cables
A method for identifying insulation defects based on 3D laser point cloud data of power cables was proposed to address the issue of low accuracy in identifying insulation defects of power cables.The original 3D laser point cloud data were enhanced by using clipping method.The 3D structural surface information of cable insulation material was obtained completely through region growing method and least square method.The edge information of surface defects and internal defects of cable insulation were solved by Canny edge detection method,and the defect location and type of power cable insulation were automatically identified.The results indicate that the as-proposed method can accurately identify scratches,blistering and holes on the insulation surface of cables,with short recognition time and relatively high robustness.It has higher practical application value.

power cableinsulation defect3D laser point cloud dataaverage normal vectorGaussian filteringgradient magnitudeCanny edge detectionhole defect

黄绪勇、林中爱、唐标、赵李强

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华中科技大学电力工程系,湖北武汉 430030

云南电网有限责任公司电力科学研究院,云南昆明 650012

昆明能讯科技有限责任公司 电网可视化平台事业部,云南 昆明 650012

电力电缆 绝缘缺陷 三维激光点云数据 平均法向量 高斯滤波 梯度幅值 Canny边缘检测 孔洞缺陷

国家重点研发计划项目云南电网有限责任公司电力科学研究院项目

2021YEE02042002021-048

2024

10.7688/j.issn.1000-1646.2024.04.08

沈阳工业大学学报
沈阳工业大学

沈阳工业大学学报

CSTPCD北大核心
影响因子：0.62
ISSN：1000-1646
年,卷(期)：2024.46(4)
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