Research on Detection Algorithm of Catenary Insulator Based on Improved Faster R-CNN

Huo, Hongtao; Min, Yongzhi; Yang, Kun; Li, Yifan

doi:10.1109/cac51589.2020.9327780

Cited by 2 publications

(1 citation statement)

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“…Kang et al [18] proposed a new insulator detection model based on the Faster R-CNN model and deep multitask neural network (DMNN), which can simultaneously segment insulators and indicate defect detection. Huo et al [19] improved the accuracy of catenary insulator detection by adding deconvolution to faster R-CNN. Insulators in railway catenary image data have the characteristics of complex background, high damage rate and unobvious failures, which increase the difficulty of detecting insulator failures.…”

Section: Introductionmentioning

confidence: 99%

Railway Insulator Detection Based on Adaptive Cascaded Convolutional Neural Network

et al. 2021

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Insulator failure is one of the important causes of railway power transmission accidents. In the automatic detection system of railway insulators, the detection and classification of insulator faults is a challenging task due to the complex background, small insulators and unobvious failures. In this article, we propose a railway insulator fault detection network based on convolutional neural network, which can detect faulty insulators from images with high resolution and complex background. The insulator fault detection network realizes the position detection and fault classification of the insulator by cascading the detection network and the fault classification network. The method of cascading two networks can reduce the amount of network calculations and improve the accuracy of fault classification. The insulator detection network uses low-resolution images for position detection, and this method can prevent the detection network from paying too much attention to the details of the image, thereby reducing the amount of network calculations. The fault classification network uses high-resolution insulator images for fault classification. The high-resolution images in this method have rich detailed information, which helps to improve the accuracy of fault classification. The trained insulator detection network and the fault classification network are cascaded to form an insulator fault detection network. The precision, recall and mAP values of the insulator fault detection network are 94.10%, 92.88% and 93.46% respectively. Experiment shows show that this network cascading method can significantly improve the accuracy and robustness of insulator fault detection.

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Section: Introductionmentioning

confidence: 99%