A Multi-feature Fusion-based Deep Learning for Insulator Image Identification and Fault Detection

et al. 2021

Applied Sciences

Insulator fault detection is one of the essential tasks for high-voltage transmission lines’ intelligent inspection. In this study, a modified model based on You Only Look Once (YOLO) is proposed for detecting insulator faults in aerial images with a complex background. Firstly, aerial images with one fault or multiple faults are collected in diverse scenes, and then a novel dataset is established. Secondly, to increase feature reuse and propagation in the low-resolution feature layers, a Cross Stage Partial Dense YOLO (CSPD-YOLO) model is proposed based on YOLO-v3 and the Cross Stage Partial Network. The feature pyramid network and improved loss function are adopted to the CSPD-YOLO model, improving the accuracy of insulator fault detection. Finally, the proposed CSPD-YOLO model and compared models are trained and tested on the established dataset. The average precision of CSPD-YOLO model is 4.9% and 1.8% higher than that of YOLO-v3 and YOLO-v4, and the running time of CSPD-YOLO (0.011 s) model is slightly longer than that of YOLO-v3 (0.01 s) and YOLO-v4 (0.01 s). Compared with the excellent object detection models YOLO-v3 and YOLO-v4, the experimental results and analysis demonstrate that the proposed CSPD-YOLO model performs better in insulator fault detection from high-voltage transmission lines with a complex background.

Section: Introductionmentioning

confidence: 99%

Insulator Faults Detection in Aerial Images from High-Voltage Transmission Lines Based on Deep Learning Model

et al. 2021

Applied Sciences

“…With recent advances in artificial intelligence and deep learning, many researchers have applied deep learning architectures to object detection, and these techniques have been variously applied [25][26][27][28]. Deep learning architectures make full use of convolution neural networks (CNNs) to automatically learn the depth feature of images layer-bylayer, and optimize the network model parameters by training large-scale data to improve detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

Electronics

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.

“…; finally, classifiers are manually designed to recognize insulators and their faults [9]. Specifically, in [10], a robust algorithm was proposed to extract insulators from aerial images with complex back- In recent years, with the rapid development of artificial intelligence technology, deep learning theory has been widely used in the fields of image classification, target detection [14][15][16][17][18], scene recognition, automatic driving of vehicles, and so on. The architectures of deep learning are composed of convolutional neural networks (CNNs), many state-of-theart algorithms based on CNNs have been put forward and successfully applied for object detection, including two-stage algorithms-Regions with Convolutional Neural Network (R-CNN) [19], Fast R-CNN [20], and Faster R-CNN [21][22][23]; and one-stage algorithms-Single Shot multi-box Detector (SSD) [24], You Only Look Once (YOLO) [25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

Energies

Insulators play a significant role in high-voltage transmission lines, and detecting insulator faults timely and accurately is important for the safe and stable operation of power grids. Since insulator faults are extremely small and the backgrounds of aerial images are complex, insulator fault detection is a challenging task for automatically inspecting transmission lines. In this paper, a method based on deep learning is proposed for insulator fault detection in diverse aerial images. Firstly, to provide sufficient insulator fault images for training, a novel insulator fault dataset named “InSF-detection” is constructed. Secondly, an improved YOLOv3 model is proposed to reuse features and prevent feature loss. To improve the accuracy of insulator fault detection, SPP-networks and a multi-scale prediction network are employed for the improved YOLOv3 model. Finally, the improved YOLOv3 model and the compared models are trained and tested on the “InSF-detection”. The average precision (AP) of the improved YOLOv3 model is superior to YOLOv3 and YOLOv3-dense models, and just a little (1.2%) lower than that of CSPD-YOLO model; more importantly, the memory usage of the improved YOLOv3 model is 225 MB, which is the smallest between the four compared models. The experimental results and analysis validate that the improved YOLOv3 model achieves good performance for insulator fault detection in aerial images with diverse backgrounds.