A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

Zhu, Jinguo; Guo, Yue; Yue, Fanding; Yuan, Huan; Yang, Aijun; Wang, Xiaohua; Rong, Mingzhe

doi:10.1109/access.2020.2995608

Cited by 37 publications

(16 citation statements)

References 40 publications

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“…One of the practical difficulties in identifying an insulator with damaged properties is that defects can be hidden under the mooring cables or under contamination [17]. Identifying a failed component during an inspection of the electrical power system requires great experience from the operator [18], even then the human factor may bring uncertainties to the process [19].…”

Section: Related Workmentioning

confidence: 99%

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

et al. 2022

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Outdoor insulators may experience stress due to severe environmental conditions, such as pollution and contamination. Through the identification of partial discharges by ultrasonic noise, it is possible to assess the possibility of a power grid failure occurring. In this paper, ensemble models are used to analyze an ultrasonic signal from an ultrasonic microphone Pettersson M500. As the insulators are susceptible to developing irreversible failures, it will be evaluated whether the ultrasonic signal will remain over time, so that it is possible to assess whether the discharges being captured can result in a failure in contaminated polymeric insulators, evaluated in a high voltage laboratory under controlled conditions. The ensemble models were used in this paper because they typically require less computational effort than techniques based on deep learning and have acceptable performance for the problem at hand. The bagging, boosting, random subspace, bagging plus random subspace, and stacked generalization ensemble models are evaluated, and the best result of each model is used to compare the differences between the models. The bagging ensemble learning model proved to be faster and have lower error than other ensemble models, long short-term memory (LSTM), and nonlinear autoregressive (NAR).

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Section: Related Workmentioning

confidence: 99%

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

et al. 2022

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“…Haoyu Xu et al proposed to identify the possible foreign objects on the airport runway by making use of the deep convolutional network [7]. Jinguo Zhu suggested detecting the foreign objects along the electric transmission lines by using the deep learning method based on the regression strategy [8]. Deqiang He put forward a object detection method combining SSD and MobileNet, which is favorable in the detection of foreign objects under the highspeed trains [9].…”

Section: Related Workmentioning

confidence: 99%

Modeling and Optimization of Semantic Segmentation for Track Bed Foreign Object Based on Attention Mechanism

Song

Wang

Gu³

et al. 2021

IEEE Access

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The problem of foreign object intrusion onto the track bed often occurs in the actual operation process of high-speed railways. To solve the problem, we propose an anomaly detection method for the ballastless track bed, which is based on semantic segmentation. Firstly, we put forward the RFODLab semantic segmentation network according to the randomness of foreign objects distribution, and a small proportion of target pixels in the track image. The segmentation results of track image obtained through this model can be used to obtain the accurate pixel information of foreign objects. To further improve the recall and precision, the channel attention mechanism is introduced for the backbone network of the model to aggregate the context information of images, which achieves the weighted constraints of the model on the area to be recognized. Furthermore, to improve the model performance affected by unbalanced sample category distribution during the anomaly detection, we modify the loss function by balancing distribution of each category. The experimental results show that our proposed method can effectively segment various types of anomalies on the ballastless track bed including broken elastic strips, animal carcasses, and fallen pieces. The precision of anomaly detection on the test set can reach 90% while the recall can be maintained at more than 95%. The anomaly detection results on actual lines also verify the effectiveness of the method.

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“…The application of object detection algorithms is becoming more and more widespread [1]. There have been many studies on applying target detection algorithms to transmission line foreign object detection [2]. These studies have also demonstrated the effectiveness of deep learning applied to foreign object detection on transmission lines.…”

Section: Introductionmentioning

confidence: 99%

Foreign object detection algorithm for high voltage transmission lines incorporating atrous convolution

Liu

2022

J. Phys.: Conf. Ser.

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There are problems of complex background, serious occlusion, and low accuracy of foreign object detection on high-voltage transmission lines. In this paper, we propose an algorithm incorporating atrous convolution for the detection of four types of foreign objects that are more likely to appear on high-voltage transmission lines: bird nests, balloons, kites, and rubbish. The algorithm is based on the original YOLOv4 model, replacing the original SPP structure with an ASPP structure. By using the atrous convolution instead of the original pooling operation, the network is able to have a larger field of perception during feature extraction, while the resolution of the feature map is not degraded too much to avoid losing too much information. The experimental results show that the accuracy of this method reaches 93.73% on the transmission line dataset used, which is a significant improvement over the original YOLOv4 model and improves the detection capability of foreign object targets that are obscured in complex environments.

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A Deep Learning Method to Detect Foreign Objects for Inspecting Power Transmission Lines

Cited by 37 publications

References 40 publications

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

Analysis of the Ultrasonic Signal in Polymeric Contaminated Insulators Through Ensemble Learning Methods

Modeling and Optimization of Semantic Segmentation for Track Bed Foreign Object Based on Attention Mechanism

Foreign object detection algorithm for high voltage transmission lines incorporating atrous convolution

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