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中國農業機械化科學研究院集團有限公司 主管

北京卓眾出版有限公司 主辦

基于YOLOv8s的雞群活動量異常監控方法

Anomaly monitoring method for chicken flock activity based on YOLOv8s

  • 摘要: 雞活動量是反映其健康的重要指標，監控雞群的活動量可以及時發現潛在的健康問題，避免疾病蔓延。提出基于YOLOv8s和BoT-SORT算法的雞群活動量異常監控方法。該方法通過攝像頭采集雞活動視頻，利用 YOLOv8s提取雞外觀和運動特征，并結合 BoT-SORT 算法實現多目標跟蹤，分析運動軌跡量化每只雞的活動量，與設定的活動量閾值自動比對，及時對異常狀態進行預警。試驗結果表明，該方法多目標跟蹤精確度（MOTP）94.3%，可有效用于雞群活動量監控并提前預警潛在問題。

     

    Abstract: Chicken activity level is an important indicator reflecting their health．By monitoring activity level of chicken flock, potential health issues can be identified in time to prevent disease spread．Therefore, an anomaly monitoring method for chicken flock activity based on YOLOv8s and BoT-SORT algorithm was proposed．Cameras were used to capture chicken activity videos, YOLOv8s was used to extract appearance and motion features of chickens, and BoT-SORT algorithm was integrated to achieve multi-object tracking．By analyzing movement trajectories, activity level of each chicken was quantified, and an automatic comparison with preset activity thresholds was done to provide timely alerts for abnormal conditions．Experimental results showed that method achieved a multi-object tracking precision（MOTP）of 94.33%, effectively monitoring chicken activity levels and providing early warnings for potential problems．

     

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