Improved YOLOv3 Network for Insulator Detection in Aerial Images with Diverse Background Interference

Abstract

Automatic inspection of insulators from high-voltage transmission lines is of paramount importance to the safety and reliable operation of the power grid. Due to different size insulators and the complex background of aerial images, it is a difficult task to recognize insulators in aerial views. Most of the traditional image processing methods and machine learning methods cannot achieve sufficient performance for insulator detection when diverse background interference is present. In this study, a deep learning method—based on You Only Look Once (YOLO)—will be proposed, capable of detecting insulators from aerial images with complex backgrounds. Firstly, aerial images with common aerial scenes were collected by Unmanned Aerial Vehicle (UAV), and a novel insulator dataset was constructed. Secondly, to enhance feature reuse and propagation, on the basis of YOLOv3 and Dense-Blocks, the YOLOv3-dense network was utilized for insulator detection. To improve detection accuracy for different sized insulators, a structure of multiscale feature fusion was adapted to the YOLOv3-dense network. To obtain abundant semantic information of upper and lower layers, multilevel feature mapping modules were employed across the YOLOv3-dense network. Finally, the YOLOv3-dense network and compared networks were trained and tested on the testing set. The average precision of YOLOv3-dense, YOLOv3, and YOLOv2 were 94.47%, 90.31%, and 83.43%, respectively. Experimental results and analysis validate the claim that the proposed YOLOv3-dense network achieves good performance in the detection of different size insulators amid diverse background interference.

Missing-Sheds Granularity Estimation of Glass Insulators Using Deep Neural Networks Based on Optical Imaging

“…Zhao et al [32] analyzed and adjusted the anchor point generation method and nonmaximum suppression in Faster R-CNN according to the different sizes, aspect ratios, and mutual occlusion of the insulators in the image. Liu et al [33] built an insulator detection method based on YOLOv3 and Dense-Blocks and combined it with a multilevel feature mapping module for different sizes of insulators and complex aerial images. Chen et al [34] proposed a detection method for foreign objects attached to lines, based on Mask R-CNN.…”

An Improved Method Based on Deep Learning for Insulator Fault Detection in Diverse Aerial Images

“…To verify the practicability of the improved YOLOv3 model proposed in this paper for insulator fault detection, experiments were conducted on four network models: YOLOv3, YOLOv3-dense [38], CSPD-YOLO [39], and our proposed model (improved YOLOv3 model). The four models were trained and then tested on the same dataset "InSF-detection" for a fair comparison.…”

“…Experimental results reveal the proposed model has a good performance in accuracy and real-time (89.96% of average precision and 0.02 s/per image in the testing set). In our previous work [38], to improve the accuracy of insulator detection with multiple sizes, YOLOv3-dense network model was proposed to perform insulator detection in aerial images with diverse background interference. Experimental results show that the average precision of the proposed model can reach 94.47%, however, since the fault area of insulator is relatively small, the fault area will be lost after the feature extraction network of YOLOv3-dense, resulting in a decrease in the accuracy of insulator fault detection.…”

“…However, there are several challenges in performing insulator fault detection based on deep learning methods, including a limited amount of insulator fault images, the extreme smallness of the insulator fault area, the complexity of the background of aerial images, and so on. To solve these issues, based on our previous work [38,39] and DenseNet, an improved YOLOv3 model is proposed for insulator fault detection in diverse aerial images from high-voltage transmission lines. To solve the problem of insufficient fault images, a novel insulator fault dataset named "InSF-detection" is created; to enhance feature reuse and feature propagation of high-resolution feature layer, DenseNets are adopted to the feature extraction network of improved YOLOv3 network model; to improve the accuracy and robustness of insulator fault detection, Spatial Pyramid Pooling networks (SPP-networks) and improved multi-scale prediction network are employed to the improved YOLOv3 network model.…”

InsulatorGAN: A Transmission Line Insulator Detection Model Using Multi-Granularity Conditional Generative Adversarial Nets for UAV Inspection

“…Zhao et al [13] improved the anchor generation method and the non-maximum suppression algorithm in the Faster R-CNN model for use with different sizes and aspect ratios and the mutual occlusion of insulators in aerial images. To enhance the reuse and spread of insulator features, Liu et al [22] added a multi-level feature mapping module based on YOLOv3 and Dense Blocks and proposed an insulator detection network called YOLOv3-dense. Focusing on the interference of complex backgrounds and small targets, the researchers in [23] proposed two insulator detection models, Exact R-CNN and CME-CNN.…”