Study on Power Line Insulator Defect Detection via Improved Faster Region-Based Convolutional Neural Network
Guo-Ping Liao, Geng-Jie Yang, Wen-Tao Tong, Wei Gao, Fang-Liang Lv, Da Gao · 2019
Machine learning based on hand-crafted features from the original aerial insulator image has shown promising results in the insulator defect detection in recent years. Such methodologies, nevertheless, are not suitable for detecting insulator defect in complex backgrounds, which intrinsically relies on sufficient prior knowledge, low background interference, and certain object scales. Therefore, in this study, an effective insulator defect detection model using improved Faster Region-based Convolutional neural network (Faster R-CNN) based on the original aerial insulator image is proposed. The soft non-maximum suppression (Soft-NMS) is utilized to improve the performance of detection overlap insulator and ResNet 101 model is adopted to effectively extract feature from insulator images. Furthermore, the proposed method has more accurate target location and higher average accuracy by comparing traditional Faster R-CNN.