Detection Method Based on Automatic Visual Shape Clustering for Pin-Missing Defect in Transmission Lines

Zhao, Zhenbing; Qi, Hongyu; Qi, Yincheng; Zhang, Ke; Zhai, Yongjie; Zhao, Wenqing

doi:10.1109/tim.2020.2969057

Cited by 90 publications

(27 citation statements)

References 27 publications

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“…In this paper, we construct a novel UAV inspection system, which can realize real-time insulator detection, positioning and automatic generation of inspection reports. Different from the existing work [41][42][43], our system can determine the true geographic coordinates of every insulator by binocular vision and realize real-time processing on the UAV's onboard terminal by embedded industrial computer. It is not only more convenient for the detection of transmission line insulators but also is crucial for UAV inspection development.…”

Section: Discussionmentioning

confidence: 99%

Real-Time Detection and Spatial Localization of Insulators for UAV Inspection Based on Binocular Stereo Vision

Chu

et al. 2021

Remote Sensing

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Unmanned aerial vehicles (UAVs) have become important tools for power transmission line inspection. Cameras installed on the platforms can efficiently obtain aerial images containing information about power equipment. However, most of the existing inspection systems cannot perform automatic real-time detection of transmission line components. In this paper, an automatic transmission line inspection system incorporating UAV remote sensing with binocular visual perception technology is developed to accurately detect and locate power equipment in real time. The system consists of a UAV module, embedded industrial computer, binocular visual perception module, and control and observation module. Insulators, which are key components in power transmission lines as well as fault-prone components, are selected as the detection targets. Insulator detection and spatial localization in aerial images with cluttered backgrounds are interesting but challenging tasks for an automatic transmission line inspection system. A two-stage strategy is proposed to achieve precise identification of insulators. First, candidate insulator regions are obtained based on RGB-D saliency detection. Then, the skeleton structure of candidate insulator regions is extracted. We implement a structure search to realize the final accurate detection of insulators. On the basis of insulator detection results, we further propose a real-time object spatial localization method that combines binocular stereo vision and a global positioning system (GPS). The longitude, latitude, and height of insulators are obtained through coordinate conversion based on the UAV’s real-time flight data and equipment parameters. Experiment results in the actual inspection environment (220 kV power transmission line) show that the presented system meets the requirement of robustness and accuracy of insulator detection and spatial localization in practical engineering.

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Section: Discussionmentioning

confidence: 99%

Real-Time Detection and Spatial Localization of Insulators for UAV Inspection Based on Binocular Stereo Vision

Chu

et al. 2021

Remote Sensing

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“…To solve this problem. Zhao et al [23] used the method of bilinear interpolation to extend the region of interest (RoI), which better retained the visual features of the inspection of small objects (tower's bolts and pins). Some scholars also propose to improve the backbone network of the detection model, such as adopting a feature pyramid network (FPN) [24] or an improved Resnet [25] to improve the feature extraction ability of tiny objects.…”

Section: Consequencementioning

confidence: 99%

Insulator Anomaly Detection Method Based on Few-Shot Learning

Wang

Gao

et al. 2021

IEEE Access

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Due to the advantages of safety and economy, it has become a trend to use unmanned aerial vehicles (UAVs) instead of humans to inspect high-voltage transmission lines. Considering the manual inspection process and the few-shot learning, a two-stage method for insulator anomaly detection is proposed.In the first stage, a positioning-restoration-cropping method is discussed for insulator string detection and processing. In the second stage, an insulator anomaly detection model called a multi-scale feature reweighting (MFR) network is built. With the help of few-shot object detection, the detection of five kinds of anomaly insulator caps, such as falling off, breakage and ablation is realized. The mean average precision (mAP) of the proposed method is 88.76%.

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“…In recent years, with the development of convolutional neural networks (CNN), object detection has been studied and applied in a wide range of fields, and its effect on object identification has been significantly improved compared with traditional methods. At present, the commonly used object detection algorithms include R-CNN [7], Faster R-CNN [8], SSD [9], YOLOv3 [10] etc., which have been applied on detections of insulator defects [11,12], pin-missing defects [13] and other electrical equipment [14]. As for the recognition and detection of birds, Tian [15] proposed a glance and stare detection (GSD) framework for capturing flying birds in aerial video, which used zooming-in algorithm to generate regional proposals and extracted adaptive depth spatio-temporal features by 3D convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

Detection of bird species related to transmission line faults based on lightweight convolutional neural network

Qiu

Zhu

Liao

et al. 2021

IET Generation Trans & Dist

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Efficient bird damage prevention of transmission lines is a long‐term challenge for power grid operation and maintenance. An approach combined lightweight convolutional neural network (CNN), image processing and object detection is presented in this paper to detect typical bird species related to transmission line faults. An image dataset of 20 bird species that threaten transmission line security is constructed. The YOLOv4‐tiny algorithm model is constructed and trained combining stage‐wise training, mosaic data enhancement, cosine annealing, and label smoothing. The mean average precision (mAP) can reach 92.04% on the test set by adjusting the parameters of the training process. Then, the validity of the proposed method is verified according to the test results and performance indexes by comparing with other methods, including Faster RCNN, SSD, YOLOv4 etc. Some image pre‐processing methods such as motion blur, defocus blur, contrast and brightness adjustment are used to simulate the scenarios in practical engineering applications. The proposed method can detect bird species perched around transmission lines with high‐efficiency, which is useful for differential prevention of bird‐related outages of power grids.

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Detection Method Based on Automatic Visual Shape Clustering for Pin-Missing Defect in Transmission Lines

Cited by 90 publications

References 27 publications

Real-Time Detection and Spatial Localization of Insulators for UAV Inspection Based on Binocular Stereo Vision

Real-Time Detection and Spatial Localization of Insulators for UAV Inspection Based on Binocular Stereo Vision

Insulator Anomaly Detection Method Based on Few-Shot Learning

Detection of bird species related to transmission line faults based on lightweight convolutional neural network

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