ORIGINAL RESEARCH article
Front. Vet. Sci.
Sec. Animal Behavior and Welfare
Volume 12 - 2025 | doi: 10.3389/fvets.2025.1674842
This article is part of the Research TopicTransforming Veterinary Medicine: Digital Tools and AI as Path to Sustainable Animal CareView all articles
S TransNeXtM: A Pig Behavior Recognition Model Based on the TransNeXtM and the sLSTM
Provisionally accepted
- 1College of Software, Shanxi Agricultural University, Taigu,Jinzhong,Shanxi, China
- 2School of Information Science and Engineering, Shanxi Agricultural University, Taigu,Jinzhong,Shanxi, China
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Pig behavior recognition serves as a crucial indicator for monitoring health and environmental conditions. However, conventional pig behavior recognition methods are limited in their ability to effectively extract image features and analyze long sequence dependencies, ultimately reducing pig behavior recognition performance. To address these challenges, we proposes a pig behavior recognition model S TransNeXtM which leverages both spatial and temporal information underlying the video. Specifically, an innovative backbone, named TransNeXtM, has been developed for the spatial domain. It incorporates a bio-inspired Aggregated Attention Mechanism, a Convolutional GLU, and a Mamba unit, which allows the model to capture more discriminative global and local features. For the temporal domain, the sLSTM is proposed to process sequence data by utilizing an exponential gating mechanism and a stabilizer state. This design allows the model to establish longer temporal sequence dependencies, outperforming conventional GRU and LSTM. Based on the above insights, the S TransNeXtM enhances the performance of pig behavior recognition. Experimental results demonstrate that the proposed S TransNeXtM model achieves the state-of-the-art performance in pig behavior recognition task. Consequently, the S TransNeXtM attains an accuracy of 94.53%, marking an improvement of up to 11.32% over previous benchmarks.
Keywords: S TransNeXtM, TransNeXtM, Temporal sequence, pig behavior recognition, SLSTM
Received: 28 Jul 2025; Accepted: 04 Sep 2025.
Copyright: © 2025 Hao, Hu, Xue, Shu and Han. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Wangli Hao, College of Software, Shanxi Agricultural University, Taigu,Jinzhong,Shanxi, China
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