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復(fù)雜環(huán)境下蛋雞個(gè)體識(shí)別與自動(dòng)計(jì)數(shù)系統(tǒng)研究
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國(guó)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(guó)家現(xiàn)代農(nóng)業(yè)產(chǎn)業(yè)技術(shù)體系項(xiàng)目(CARS-40)、河北省科技研發(fā)平臺(tái)建設(shè)專項(xiàng)(225676150H)和河北省在讀研究生創(chuàng)新能力項(xiàng)目(CXZZSS2023055)


Individual Identification and Automatic Counting System of Laying Hens under Complex Environment
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    摘要:

    雞群計(jì)數(shù)是雞場(chǎng)資產(chǎn)評(píng)估中一項(xiàng)非常重要的工作,。目前雞場(chǎng)采用的人工計(jì)數(shù)方法,,存在效率低下且計(jì)數(shù)準(zhǔn)確度不穩(wěn)定的問(wèn)題。針對(duì)此問(wèn)題,,本文提出了一種基于改進(jìn)YOLO v5s的蛋雞個(gè)體識(shí)別與計(jì)數(shù)的方法,。該方法為了消除真實(shí)復(fù)雜環(huán)境下產(chǎn)蛋箱、食槽等設(shè)施對(duì)蛋雞個(gè)體識(shí)別帶來(lái)的干擾,,在YOLO v5s模型的Neck部分引入了SimAM注意力機(jī)制,;為了擴(kuò)大模型感受野,解決蛋雞個(gè)體較小,、識(shí)別困難的問(wèn)題,,將YOLO v5s模型的SPPF(空間金字塔池化模塊)改為了SPPCSPC模塊;為了盡可能多地提取蛋雞有效特征,,通過(guò)在YOLO v5s的Neck結(jié)構(gòu)添加自適應(yīng)特征融合模塊ASFF,,將不同尺度的蛋雞成像特征信息進(jìn)行融合的方法,進(jìn)一步提升了模型的檢測(cè)精度,。在此基礎(chǔ)上,,通過(guò)調(diào)用模型檢測(cè)接口,,在接口內(nèi)部添加計(jì)數(shù)函數(shù)、統(tǒng)計(jì)目標(biāo)數(shù)量的方法,,實(shí)現(xiàn)了蛋雞個(gè)體的計(jì)數(shù)和雞舍飼養(yǎng)密度的計(jì)算,。將改進(jìn)后的模型通過(guò)PyQt工具包進(jìn)行封裝、打包,,開發(fā)了蛋雞個(gè)體識(shí)別與自動(dòng)計(jì)數(shù)系統(tǒng),。實(shí)驗(yàn)結(jié)果表明,改進(jìn)的YOLO v5s模型的精準(zhǔn)率,、召回率,、平均精度均值分別為89.91%、79.24%,、87.53%,,較YOLO v5s模型分別提高2.37、2.55,、2.20個(gè)百分點(diǎn),。本模型在120~247只蛋雞雞舍的計(jì)數(shù)平均準(zhǔn)確率為94.77%,較YOLO v5s模型提升2.49個(gè)百分點(diǎn),。研發(fā)的蛋雞計(jì)數(shù)系統(tǒng)在河北省某養(yǎng)殖基地得到了實(shí)際應(yīng)用,,為養(yǎng)殖場(chǎng)的蛋雞數(shù)量清點(diǎn)提供了一種可靠且有效的方法。

    Abstract:

    Hen counting is a very important task in hen farm asset valuation. The current manual counting methods used in hen farms suffer from low efficiency and unstable counting accuracy. To resolve this problem, a method for identifying and counting individual laying hens was proposed based on improved YOLO v5s. The method introduced the SimAM attention mechanism in the Neck part of the YOLO v5s model in order to eliminate the interference brought by facilities such as laying boxes and feeding troughs on the identification of individual laying hens in the real complex environment; in order to expand the sensory field of the model and solve the problem of small individual laying hens and difficulties in identification, the spatial pyramid pooling module (SPPF) of the YOLO v5s model was replaced by the SPPCSPC module; in order to extract as many effective features of laying hens as possible, the detection accuracy of the model was further improved by adding the adaptive feature fusion module ASFF to the Neck structure of YOLO v5s, which fused the imaging feature information of laying hens at different scales. On this basis, the counting of individual laying hens and the calculation of the housing density were realized by calling the model detection interface and adding counting functions and counting target numbers inside the interface. The improved model was packaged by PyQt toolkit, and the system of individual laying hens identification and automatic counting was developed. The test results showed that the precision, recall and mAP of the improved YOLO v5s model were 89.91%, 79.24% and 87.53%, respectively, which were 2.37, 2.55 and 2.20 percentage points higher than those of the YOLO v5s model. The average accuracy of this model in counting 120~247 laying hen houses was 94.77%, which was 2.49 percentage points better than that of the YOLO v5s model. The laying hens counting system developed was applied in a farm base in Hebei, providing a reliable and effective method for counting the number of laying hens on a farm.

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楊斷利,王永勝,陳輝,孫二東,王連增.復(fù)雜環(huán)境下蛋雞個(gè)體識(shí)別與自動(dòng)計(jì)數(shù)系統(tǒng)研究[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2023,54(6):297-306. YANG Duanli, WANG Yongsheng, CHEN Hui, SUN Erdong, WANG Lianzeng. Individual Identification and Automatic Counting System of Laying Hens under Complex Environment[J]. Transactions of the Chinese Society for Agricultural Machinery,2023,54(6):297-306.

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  • 收稿日期:2023-02-28
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  • 在線發(fā)布日期: 2023-04-20
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