PAPER P13 · 2020
Detection of Power Line Insulator Defects Using Aerial Images Analyzed With Convolutional Neural Networks
Tao, X., Zhang, D., Wang, Z., Liu, X., Zhang, H., Xu, D. — IEEE Transactions on Systems, Man, and Cybernetics: Systems 50(4):1486-1498
已读摘要 引用次数: openalex: 775 · semanticScholar: 684 (2026-10-22)
提出一种级联 CNN，先在航拍图像中定位绝缘子，再在其中检测缺陷，将任务拆分为两个检测层级。由于航拍背景杂乱，早期基于人工特征或浅层方法只能在狭窄条件下奏效。缺陷图像稀缺的问题通过仿射变换、分割后粘贴到新背景、高斯模糊和亮度变化来解决。
主要发现
- Precision 0.91 and recall 0.96 on a standard insulator dataset
- Synthetic augmentation (insulator segmentation and background fusion) used to overcome scarce defect images
局限
- Abstract only reviewed
- Evaluation on one dataset; defect type mainly missing insulator caps
- Heavy reliance on synthetic augmentation may not reflect real defect variety