Title: A power image autonomous recognition method based on improved regional full convolution network

Authors: Shuhua Liang; Yansong Sun; Dalei Wu; Xian Yang; Jiaying Li; Lei Gao

Addresses: Hainan Power Grid Co., Ltd., Haikou, 570311, Hainan, China ' Hainan Power Grid Co., Ltd., Haikou, 570311, Hainan, China ' Hainan Power Grid Co., Ltd., Haikou, 570311, Hainan, China ' Hainan Power Grid Co., Ltd., Haikou, 570311, Hainan, China ' Hainan Power Grid Co., Ltd., Haikou, 570311, Hainan, China ' Hainan Power Grid Co., Ltd., Haikou, 570311, Hainan, China

Abstract: In view of the poor recognition effect caused by the interference of electromagnetic wave, external environment and other factors in the process of power image acquisition, an autonomous recognition method of power image based on improved regional full convolution network is proposed. Firstly, the power image is collected and the interference factors are analysed. Based on this, the image pre-processing is completed. Secondly, the offset and weight are introduced to increase the receptive field of the standard grid to improve the regional full convolution network. Then, the improved regional full convolution network is applied to construct the power image autonomous recognition model to realise the power image recognition function. Ultimately, empirical trials are conducted to substantiate the progressiveness of the suggested approach. The outcomes reveal that the detection precision of the proposed method for power imagery surpasses 94.32%, and it exhibits superior accuracy and recall rates.

Keywords: full convolutional neural network; power image; autonomous recognition; smart grid.

DOI: 10.1504/IJSCC.2025.149355

International Journal of Systems, Control and Communications, 2025 Vol.16 No.4, pp.294 - 309

Received: 30 Dec 2024
Accepted: 07 Apr 2025
Published online: 27 Oct 2025 *

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