Title: A fast identification method of UAV inspection image target for substation equipment

Authors: Wen Kang; You Li; DeKai Liu; JieXue Zheng; Yisheng Yu

Addresses: State Grid Hunan Extra High Voltage Substation Company, Changsha 410004, China; Substation Intelligent Operation and Inspection Laboratory of State Grid Hunan Electric Power Co., Ltd, Changsha 410004, China ' State Grid Hunan Extra High Voltage Substation Company, Changsha 410004, China; Substation Intelligent Operation and Inspection Laboratory of State Grid Hunan Electric Power Co., Ltd, Changsha 410004, China ' Whayer Intelligent Technology Group Co., Ltd., Chengdu 610041, China ' Whayer Intelligent Technology Group Co., Ltd., Chengdu 610041, China ' State Grid Hunan Extra High Voltage Substation Company, Changsha 410004, China

Abstract: Aiming at the problems of low accuracy, long time, and poor recognition effects of traditional methods, a fast identification method of UAV inspection image target for substation equipment is proposed. Firstly, Gabor filter is used to extract the target features of UAV patrol image of substation equipment, and the UAV patrol image is segmented. Then, the binary image is processed, and the constraint criterion of image reconstruction is established to realise image reconstruction. Finally, all the image blocks are normalised, and the target model is constructed on the basis of prior knowledge to match and find the corresponding target, so as to realise the rapid recognition of the image target of UAV patrol inspection of substation equipment. The experimental results show that the target recognition accuracy of this method is always higher than 70%, and the minimum recognition time is only 3.26 s. The target recognition effect is good.

Keywords: substation equipment; UAV patrol inspection; image recognition; Gabor filter; image reconstruction; prior knowledge; image segmentation; binarisation.

DOI: 10.1504/IJRIS.2023.136352

International Journal of Reasoning-based Intelligent Systems, 2023 Vol.15 No.3/4, pp.220 - 227

Received: 29 Jun 2022
Accepted: 16 Sep 2022
Published online: 31 Jan 2024 *

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