Research on automatic location and recognition of insulators in substation based on YOLOv3

Liu, Yunpeng; Ji, Xinxin; Pei, Shaotong; Ma, Zhen; Zhang, Gonghao; Lin, Ying; Chen, Yufeng

doi:10.1049/hve.2019.0091

Cited by 50 publications

(33 citation statements)

References 16 publications

(20 reference statements)

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“…YOLOv3 [ 3 ] evolved from YOLOv1 [ 33 ] and YOLOv2 [ 34 ]. High speed and accuracy make it a popular method for vision applications [ 35 , 36 , 37 ]. The basic idea behind the YOLO algorithm is as follows.…”

Section: Methodsmentioning

confidence: 99%

Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Tsai

Lai

Sun

et al. 2021

Sensors

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Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

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Section: Methodsmentioning

confidence: 99%

Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Tsai

Lai

Sun

et al. 2021

Sensors

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“…The core of this method lies in the construction of image processing and recognition algorithms. The difficulty is that the method needs to have good real-time, accuracy and robustness to the background [4]. The thesis focuses on improving the detection accuracy in the substation scenario.…”

Section: Motivationmentioning

confidence: 99%

“…Then we calculate mAP and F-Measure by precision rate (P) and recall rate (R). mAP is calculated by (3), and F-Measure is calculated by (4).…”

Section: Experimental Data Setmentioning

confidence: 99%

Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm

et al. 2021

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Aiming at the problem that the number and decimal point of digital instruments in substations are prone to misdetection and missed detection, a method of digital meter readings in a substation based on connected domain analysis algorithm is proposed. This method uses Faster R-CNN (Faster Region Convolutional Neural Network) as a positioning network to localize the dial area, and after acquiring the partial image, it enhances the useful information of the digital area. YOLOv4 (You Only Look Once) convolutional neural network is used as the detector to detect the digital area. The purpose is to distinguish the numbers and obtain the digital area that may contain a decimal point or no decimal point at the tail. Combined with the connected domain analysis algorithm, the difference between the number of connected domain categories and the area ratio of the digital area is analyzed, and the judgment of the decimal point is realized. The method reduces the problem of mutual interference among categories when detecting YOLOv4. The experimental results show that the method improves the detection accuracy of the algorithm.

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“…In this method, a single multi‐frame detection (SSD) algorithm is adopted, and the recognition accuracy of porcelain vases and conforming insulators can reach 94.1% and 86.7% respectively. In [5], you only look once version3 (YOLOV3) is mainly used to realise the detection algorithm framework of insulation equipment. Under the condition of high recognition accuracy, the average detection time of the object is 0.…”

Section: Introductionmentioning

confidence: 99%

An insulator inspection method based on deep learning applicable to multi‐scale and occlusion conditions

Tang

2021

J. eng.

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As an important equipment of power system, the insulator's normal operation is the basis to ensure the safe operation of the power system. The insulator positioning and identification technology based on machine vision can quickly and accurately complete the inspection of insulators on site and effectively save the cost of operation and maintenance. This paper proposes an insulator inspection method based on region-convolutional neural networks (RCNNs). First, the dataset of the insulator image is preprocessed by means of data expansion. Then, the feature extraction of the insulator image is realised by using the zeiler fergus (ZF) network. The k-means clustering method is used to optimise the selection of anchor points. Meanwhile, the non-maximum suppression post-processing algorithm is improved, and a non-linear penalty factor is introduced to adapt to multi-scale and overlapping occlusion insulator inspection. Experimental results show that the improved faster RCNNs insulator inspection method can accurately obtain the coordinate frame and the corresponding probability value of the insulator object and improve the average precision by 10.43%, achieving the accurate inspection of the insulator object.

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Research on automatic location and recognition of insulators in substation based on YOLOv3

Cited by 50 publications

References 16 publications

Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm

An insulator inspection method based on deep learning applicable to multi‐scale and occlusion conditions

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