基于双层路由注意力和自校准卷积的豹个体识别

doi:10.3969/j.issn.0255-8297.2025.02.012

Abstract

Abstract: Infrared camera images of leopards in natural environments pose significant challenges for individual recognition due to issues such as high fusion between individuals and their surroundings, as well as high inter-class similarity. To address these challenges, an improved EfficientNet model is proposed, incorporating self-calibrating convolution and bilevel routing attention. The self-calibrating convolution adaptively builds remote space and inter-channel dependencies around each spatial location. The ability to recognize detailed features is enhanced by explicitly combining richer contextual information. This effectively mitigates the recognition challenges posed by high inter-class similarity. Meanwhile, the bilevel routing attention combines the top-down global attention strategy and the bottom-up local attention strategy to solve the problem of high integration between individuals and their environment. Experiment results show that the accuracy of the proposed model reaches 95.56% in the task of leopard individual recognition, which is significantly higher than the original EfficientNet. These findings validate the effectiveness and superiority of the proposed model in dealing with leopard individual recognition task.

Key words: individual recognition, self-calibrating convolution, bi-level routing attention, deep learning, self-built data set

CLC Number:

TP391.4

YANG Wan, CHEN Aibin, ZHAO Ying, WU Yue, ZHEN Xin, XIAO Zhishu. Leopards Individual Recognition Based on Bi-level Routing Attention and Self-Calibrated Convolution[J]. Journal of Applied Sciences, 2025, 43(2): 348-360.

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Leopards Individual Recognition Based on Bi-level Routing Attention and Self-Calibrated Convolution

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