基于L-ConvNeXt网络的变电站人员安全操作检测方法

doi:10.16088/j.issn.1001-6600.2023030401

Abstract

Abstract: A substation personnel detection based on the L-ConvNeXt network is proposed to solve the problems of excessive amount of network parameters and unclear characteristics of operational personnel detection in the complex environment of substations. Firstly, the backbone feature extraction network is built by the lightweight ConvNeXt to enhance the feature extraction capability of the network while keeping the backbone with a low parameter. Then, the transformer prediction head(TPH) is selected as the end detection head to enhance the detection of low-resolution features by the network. Finally, VariFocal Loss is introduced to replace Focal Loss as target loss and confidence loss to improve the loss weight of the network for positive samples. The experimental results on the public dataset of Tianchi show that the proposed network achieves a better detection results, the average detection accuracy reaches 89.6% and the number of model parameters is 13.2×10⁶, which can effectively detect the operation of substation personnel and meet the detection requirements of substation operators in complex scenarios.

Key words: object detection, ConvNeXt, lightweight model, substation, VariFocal Loss

CLC Number: TP391.41

ZENG Liang, HU Qian, YANG Tengfei, TAN Weiwei. Substation Personnel Safety Operation Detection Based on L-ConvNeXt Network[J].Journal of Guangxi Normal University(Natural Science Edition), 2024, 42(1): 102-110.

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Substation Personnel Safety Operation Detection Based on L-ConvNeXt Network

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