ArticleAnimals : an open access journal from MDPI2023
Efficient Aggressive Behavior Recognition of Pigs Based on Temporal Shift Module.
Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Unlocking the potential of computer vision in precision pig farming: a call for a collaborative data and AI models platform.Animal frontiers : the review magazine of animal agriculture · 2026Article
- Integrating Deep Learning and Transcriptomics to Assess Livestock Aggression: A Scoping Review.Biology · 2025Review
- Review
- Recognition of Foal Nursing Behavior Based on an Improved RT-DETR Model.Animals : an open access journal from MDPI · 2025Article
- S_TransNeXtM: a pig behavior recognition model based on the TransNeXtM and the sLSTM.Frontiers in veterinary science · 2025Article
- A Real-Time Lightweight Behavior Recognition Model for Multiple Dairy Goats.Animals : an open access journal from MDPI · 2024Article
- Sleeping and Eating Behavior Recognition of Horses Based on an Improved SlowFast Network.Sensors (Basel, Switzerland) · 2024Article
- Tracking and Behavior Analysis of Group-Housed Pigs Based on a Multi-Object Tracking Approach.Animals : an open access journal from MDPI · 2024Article
- A Long-Term Video Tracking Method for Group-Housed Pigs.Animals : an open access journal from MDPI · 2024Article
- A Functional Single Nucleotide Polymorphism in the 3' Untranslated Region of the Porcine JARID2 Gene Is Associated with Aggressive Behavior of Weaned Pigs after Mixing.International journal of molecular sciences · 2023Article
- Pig Movement Estimation by Integrating Optical Flow with a Multi-Object Tracking Model.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
Aggressive behavior among pigs is a significant social issue that has severe repercussions on both the profitability and welfare of pig farms. Due to the complexity of aggression, recognizing it requires the consideration of both spatial and temporal features. To address this problem, we proposed an efficient method that utilizes the temporal shift module (TSM) for automatic recognition of pig aggression. In general, TSM is inserted into four 2D convolutional neural network models, including ResNet50, ResNeXt50, DenseNet201, and ConvNext-t, enabling the models to process both spatial and temporal features without increasing the model parameters and computational complexity. The proposed method was evaluated on the dataset established in this study, and the results indicate that the ResNeXt50-T (TSM inserted into ResNeXt50) model achieved the best balance between recognition accuracy and model parameters. On the test set, the ResNeXt50-T model achieved accuracy, recall, precision, F1 score, speed, and model parameters of 95.69%, 95.25%, 96.07%, 95.65%, 29 ms, and 22.98 M, respectively. These results show that the proposed method can effectively improve the accuracy of recognizing pig aggressive behavior and provide a reference for behavior recognition in actual scenarios of smart livestock farming.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.