ArticleComputational intelligence and neuroscience2022
Efficient Detection Method of Pig-Posture Behavior Based on Multiple Attention Mechanism.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 21 citations in OpenAlex.
- SowPostureDS: A Multi-Class Image Dataset for YOLO-Based Detection of Sow Postures in diverse Farrowing Systems.Scientific data · 2026Article
- Characterising responses in group-housed pigs to Salmonella typhimurium infection through integrated computer vision-based behavioural monitoring and statistical analyses.Porcine health management · 2026Article
- MMBs_TransNeXt: a hierarchical adaptive feature learning model for pig behavior recognition in precision livestock farming.Frontiers in veterinary science · 2026Article
- Pig Health Assessment Framework Based on Behavioural Analysis.Animals : an open access journal from MDPI · 2025Article
- Instance segmentation and automated pig posture recognition for smart health management.Journal of animal science and technology · 2025Article
- S_TransNeXtM: a pig behavior recognition model based on the TransNeXtM and the sLSTM.Frontiers in veterinary science · 2025Article
- Enhanced Swine Behavior Detection with YOLOs and a Mixed Efficient Layer Aggregation Network in Real Time.Animals : an open access journal from MDPI · 2024Article
- Behavior Tracking and Analyses of Group-Housed Pigs Based on Improved ByteTrack.Animals : an open access journal from MDPI · 2024Article
- Pattern Mining-Based Pig Behavior Analysis for Health and Welfare Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Efficient Aggressive Behavior Recognition of Pigs Based on Temporal Shift Module.Animals : an open access journal from MDPI · 2023Article
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Authors and funding
6 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Due to the low detection precision and poor robustness, the traditional pig-posture and behavior detection method is difficult to apply in the complex pig captivity environment. In this regard, we designed the HE-Yolo (High-effect Yolo) model, which improves the Darknet-53 feature extraction network and integrates DAM (Dual attention mechanism) of channel attention mechanism and space attention mechanism, to recognize the posture behaviors of the enclosure pigs in real-time. First, the pig data set is clustered and optimized by the K-means algorithm to obtain a new anchor frame size. Second, the DSC (Depthwise separable convolution) and h-switch activation function are innovatively introduced into the Darknet-53 feature extraction network, and the C-Res (Contrary residual structure) unit is designed to build Darknet-A feature extraction network, so as to avoid network gradient explosion and ensure the integrity of feature information. Subsequently, DAM integrating the spatial attention mechanism and the channel attention mechanism is established, and it is further combined with the Incep-abate module to form DAB (Dual attention block), and HE-Yolo is finally built by Darknet-A and DAB. A total of 2912 images of 46 enclosure pigs are divided into the training set, the verification set, and the test set according to the ratio of 14 : 3:3, and the recognition performance of HE-Yolo is verified according to the parameters of the precision
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