An improved Faster R-CNN for defect recognition of key components of transmission line

• Main Authors: Hongxia Ni, Minzhen Wang, Liying Zhao
• Format: Article
• Language: English
• Published: AIMS Press 2021-05-01
• Series: Mathematical Biosciences and Engineering
• Subjects: convolutional neural networks; defect identification; faster r-cnn; inception-resnet-v2; target detection
• Online Access: http://www.aimspress.com/article/doi/10.3934/mbe.2021237?viewType=HTML

In a national power grid system, it is necessary to keep transmission lines secure. Detection and identification must be regularly performed for transmission tower components. In this paper, we propose a defect recognition method for key components of transmission lines based on deep learning. First, based on the characteristics of the transmission line image, the defect images are preprocessed, and the defect dataset is created. Then, based on the TensorFlow platform and the traditional Faster R-CNN based on the R-CNN model, the concept-ResNet-v2 network is used as the basic feature extraction network to improve the network structure adjustment and parameter optimization. Through feature extraction, target location, and target classification of aerial transmission line defect images, a target detection model is obtained. The model improves the feature extraction on transmission line targets and small target component defects. The experimental results show that the proposed method can effectively identify the defects of key components of the transmission lines with a high accuracy of 98.65%.