基于CBCW-YOLO v8的猪只行为识别方法研究  

Pig Behavior Recognition Based on CBCW-YOLO v8 Model

作　　者：仝志民 徐天哲 石传淼 李盛章 谢秋菊[2] 荣丽红 TONG Zhimin;XU Tianzhe;SHI Chuanmiao;LI Shengzhang;XIE Qiuju;RONG Lihong(College of Mechanical and Electrical Engineering,Qingdao Agricultural University,Qingdao 266109,China;College of Electrical and Information,Northeast Agricultural University,Harbin 150030,China)

机构地区：[1]青岛农业大学机电工程学院,青岛266109 [2]东北农业大学电气与信息学院,哈尔滨150030

出　　处：《农业机械学报》2025年第2期411-419,共9页Transactions of the Chinese Society for Agricultural Machinery

基　　金：青岛农业大学博士启动基金项目(1121005);农业农村部华南热带智慧农业技术重点实验室开放课题(HNZHNY-KFKT-202206);山东省科技型中小企业创新能力提升工程项目(2023TSGC0741);国家自然科学基金面上项目(32372934)。

摘　　要：随着现代生猪养殖业快速发展,对猪只行为精准识别需求日益增长。针对猪只行为多样性、特征相似性、相互遮挡和堆积等问题,提出一种基于改进YOLO v8模型的猪只行为识别方法。首先,引入ConvNeXt V2作为主干特征提取网络,以增强对检测目标的语义信息提取能力。其次,在特征融合网络中添加加权双向特征金字塔网络(BiFPN),强化模型特征融合能力。此外,结合上采样算子CARAFE,进一步提升模型在行为识别过程中特征提取能力。最后,使用WIoUv3作为损失函数,优化模型检测精度。经实验验证,改进后模型准确率、召回率、平均精度均值和F1值分别达到89.6%、88.0%、91.9%和88.8%,与TOOD、YOLO v7和YOLO v8模型相比,平均精度均值分别提高10.9、6.3、3.7个百分点,显著提高猪只行为识别精度。消融实验表明,各项改进均对模型的识别性能有提升效果,ConvNeXt V2主干特征提取网络对模型的提升效果最明显。综上所述,CBCW-YOLO v8模型在猪只行为识别任务中展现出优良的综合性能,为猪只健康管理和疾病预警提供有力的技术支持。With the rapid development of modern pig breeding industry,the demand for precise recognition of pig behaviors is increasing.Aiming to address the issues of diversity of pig behaviors,similarity of features,mutual occlusion and stacking,a pig behavior recognition method based on the improved YOLO v8 model was proposed.Firstly,the ConvNeXt V2 was introduced as the backbone feature extraction network to enhance the ability to extract semantic information of the detection target.Secondly,the bi-directional feature pyramid network(BiFPN)was added to the feature fusion network to enhance the feature fusion ability of the model.Thirdly,combined with the CARAFE up-sampling operator,the feature extraction ability of the model in the process of behavior recognition was further improved.Finally,the WIoUv3 was used as the loss function to optimize the detection accuracy of the model.The experimental results showed that the precision rate,recall rate,mean average precision and F1 value of the improved model reached 89.6%,88.0%,91.9%and 88.8%,respectively.Compared with TOOD,YOLO v7 and YOLO v8 models,the mean average precision was increased by 10.9,6.3 and 3.7 percentage points,respectively,which significantly improved the accuracy of pig behavior recognition.The ablation experiments showed that all the improvements improved the recognition performance of the model,and the ConvNeXt V2 backbone feature extraction network had the most obvious improvement effect on the model.In summary,the CBCW-YOLO v8 model demonstrated excellent overall performance in pig behavior recognition tasks and provided powerful technical support for pig health management and disease early warning.

关 键 词：猪只行为识别 YOLO v8 特征提取网络 CARAFE WIoUv3 目标检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]