Insulator Detection Method in Inspection Image Based on Improved Faster R-CNN

Zhenbing Zhao, Zhen Zhen, Lei Zhang, Yincheng Qi, Yinghui Kong and Ke Zhang
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Zhenbing Zhao: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Zhen Zhen: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Lei Zhang: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Yincheng Qi: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Yinghui Kong: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Ke Zhang: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China

Energies, 2019, vol. 12, issue 7, 1-15

Abstract: The detection of insulators in power transmission and transformation inspection images is the basis for insulator state detection and fault diagnosis in thereafter. Aiming at the detection of insulators with different aspect ratios and scales and ones with mutual occlusion, a method of insulator inspection image based on the improved faster region-convolutional neural network (R-CNN) is put forward in this paper. By constructing a power transmission and transformation insulation equipment detection dataset and fine-tuning the faster R-CNN model, the anchor generation method and non-maximum suppression (NMS) in the region proposal network (RPN) of the faster R-CNN model were improved, thus realizing a better detection of insulators. The experimental results show that the average precision (AP) value of the faster R-CNN model was increased to 0.818 with the improved anchor generation method under the VGG-16 Net. In addition, the detection effect of different aspect ratios and different scales of insulators in the inspection images was improved significantly, and the occlusion of insulators could be effectively distinguished and detected using the improved NMS.

Keywords: insulator; Faster R-CNN; object detection; RPN; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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