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基于改進(jìn)YOLO v8n模型的散養(yǎng)蛋雞個(gè)體行為識別方法與差異分析
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國家自然科學(xué)基金項(xiàng)目(32172779)、財(cái)政部和農(nóng)業(yè)農(nóng)村部:國家現(xiàn)代農(nóng)業(yè)產(chǎn)業(yè)技術(shù)體系項(xiàng)目(CARS-40),、河北省現(xiàn)代農(nóng)業(yè)產(chǎn)業(yè)技術(shù)體系建設(shè)專項(xiàng)資金項(xiàng)目(HBCT2023210201),、邯鄲市科學(xué)技術(shù)研究與發(fā)展計(jì)劃項(xiàng)目(22313014017)和河北省省屬高等學(xué)校基本科研業(yè)務(wù)費(fèi)研究項(xiàng)目(KY2023050)


Individual Behavioral Identification and Differential Analysis of Free-range Laying Hens Based on Improved YOLO v8n Model
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    摘要:

    家禽行為與其生理狀態(tài)密切相關(guān),,可利用行為數(shù)據(jù)對家禽健康狀況進(jìn)行評估,。統(tǒng)計(jì)個(gè)體行為數(shù)據(jù)需要進(jìn)行蛋雞行為識別和個(gè)體身份識別,,針對行為識別過程中,,蛋雞體型小、聚集遮擋,,養(yǎng)殖環(huán)境光照變化等因素導(dǎo)致的蛋雞有效特征表達(dá)不足,,個(gè)體行為識別效果不理想問題,基于YOLO v8n網(wǎng)絡(luò)構(gòu)建行為識別模型,,同時(shí)融合ODConv,、GhostBottleneck,、GAM注意力和Inner-IoU結(jié)構(gòu),通過減少圖像特征丟失,,放大全局交互信息,,融合跨階段特征,增強(qiáng)特征提取及泛化能力對模型進(jìn)行改進(jìn),,提升了蛋雞采食,、飲水、站立,、整理羽毛,、俯身搜索5種行為的識別精度。同時(shí)基于YOLO v8n模型構(gòu)建了個(gè)體身份識別網(wǎng)絡(luò),,并通過引入MobileNetV3模塊對個(gè)體身份識別網(wǎng)絡(luò)模型進(jìn)行優(yōu)化,,提升了個(gè)體行為數(shù)據(jù)統(tǒng)計(jì)效率。試驗(yàn)結(jié)果表明,,優(yōu)化后行為識別模型對采食,、飲水、站立,、整理羽毛,、俯身搜索行為識別平均精度(AP)分別達(dá)到94.4%、93.0%,、90.7%,、91.7%、86.9%,,平均精度均值(mAP)達(dá)到91.4%,,與YOLO v5n、YOLO v6n,、YOLO v7-tiny,、YOLO v8n相比,平均精度均值(mAP)分別提高4.8,、4.1,、5.5、3.5個(gè)百分點(diǎn),;個(gè)體身份識別模型參數(shù)量和運(yùn)算量與YOLO v8n模型相比,,減少1.9651×106和6.1×109。通過分析蛋雞行為數(shù)據(jù)發(fā)現(xiàn),,行為數(shù)據(jù)與溫度及蛋雞個(gè)體本身有關(guān),,溫度降低時(shí),采食、站立次數(shù)增加,,飲水次數(shù)減少,,整理羽毛、俯身搜索次數(shù)幾乎無變化,,相同溫度下,,不同蛋雞個(gè)體的行為數(shù)據(jù)差異較大,且差異值與蛋雞體型有關(guān),。試驗(yàn)結(jié)果為依據(jù)行為數(shù)據(jù)評判蛋雞健康狀況,、養(yǎng)殖場精準(zhǔn)養(yǎng)殖及蛋雞個(gè)體優(yōu)選奠定了基礎(chǔ)。

    Abstract:

    Poultry behavior is closely related to its physiological state, and behavioral data can be used to assess the health status of poultry. Statistical individual behavioral data is needed for laying hen behavioral identification and individual identification, to address the behavioral identification process, laying hen body size was small, aggregation of shade, breeding environment lighting changes and other factors resulting in the laying hen effective features expression was insufficient, individual behavioral identification effect was not ideal problem, based on the YOLO v8n network to build behavioral identification model, while fusing ODConv, GhostBottleneck, GAM attention and Inner-IoU structure, and the model was improved by reducing image feature loss, amplifying global interaction information, fusing crossstage features, and enhancing the feature extraction and generalization ability, which improved the recognition accuracy of five behaviors of laying hens, namely, feeding, drinking, standing, feather arranging, and stooping to search. Meanwhile, the individual identification network was constructed based on the YOLO v8n model, and the individual identification network model was optimized by introducing the MobileNetV3 module, which improved the statistical efficiency of individual behavioral data. The experimental results showed that the optimized behavior identification model achieved 94.4%, 93%, 90.7%, 91.7%, 86.9% average precision (AP) for the recognition of feeding, drinking, standing, feather arranging, and stooping searching behaviors, respectively, and 91.4% mean average precision (mAP), which was comparable to that of YOLO v5n, YOLO v6n, and YOLO v7-tiny, YOLO v8n, the mean average precision mean (mAP) was increased by 4.8, 4.1, 5.5, and 3.5 percentage points, respectively;the number of parameters and the amount of operations of the individual identification model were reduced by 1.9651×106 and 6.1×109 compared with that of the YOLO v8n model.It was found that by analyzing the behavioral data of the laying hens, the behavioral data were related to the temperature and the individual laying hens themselves, and that when the temperature was decreased, the number of feeding and standing was increased, the number of drinking was decreased, the number of finishing feathers and stooping to search almost did not change, the behavioral data of different individual laying hens varied greatly at the same temperature, and the value of the difference was related to the body size of the laying hens. The results of the experiment laid the foundation for judging the health status of laying hens based on behavioral data, precision breeding on farms and preferential selection of individual laying hens.

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楊斷利,齊俊林,陳輝,高媛,王連增.基于改進(jìn)YOLO v8n模型的散養(yǎng)蛋雞個(gè)體行為識別方法與差異分析[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2024,55(11):112-123. YANG Duanli, QI Junlin, CHEN Hui, GAO Yuan, WANG Lianzeng. Individual Behavioral Identification and Differential Analysis of Free-range Laying Hens Based on Improved YOLO v8n Model[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(11):112-123.

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  • 收稿日期:2024-07-08
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  • 在線發(fā)布日期: 2024-11-10
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