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非接觸式籠養(yǎng)蛋雞核心體溫檢測(cè)方法
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國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2021YFD1300100)和政府間國(guó)際科技創(chuàng)新合作重點(diǎn)項(xiàng)目(2018YFE0128100)


Non-contact Core Body Temperature Detection Method for Caged Laying Hens
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    摘要:

    針對(duì)籠養(yǎng)條件下蛋雞核心溫度測(cè)量工作效率低下的問(wèn)題,提出了一種利用紅外熱圖像結(jié)合深度學(xué)習(xí)的蛋雞核心溫度檢測(cè)方法。首先通過(guò)采集172只蛋雞的10994幅紅外熱圖像制作數(shù)據(jù)集,利用目標(biāo)檢測(cè)網(wǎng)絡(luò)YOLO v8s提取作為感興趣區(qū)域(Region of interest, ROI)的雞臉圖像;再利用改進(jìn)的深度卷積神經(jīng)網(wǎng)絡(luò)對(duì)提取的蛋雞ROI圖像以及實(shí)時(shí)采集的蛋雞泄殖腔溫度進(jìn)行回歸預(yù)測(cè)。實(shí)驗(yàn)顯示,目標(biāo)檢測(cè)算法的檢測(cè)準(zhǔn)確率達(dá)到99.38%,平均精度均值達(dá)到99.9%,召回率達(dá)到99.87%,3項(xiàng)評(píng)價(jià)指標(biāo)均高于YOLO v4s、YOLO v5s、YOLO v7、YOLOX-s目標(biāo)檢測(cè)算法;在深度卷積神經(jīng)網(wǎng)絡(luò)算法上,同時(shí)將MobileNetV3、GhostNet、ShuffleNetV2、RegNet、ConvNeXt、Res2Net以及MobileVIT共7種分類(lèi)模型修改為回歸模型,利用蛋雞ROI圖像進(jìn)行訓(xùn)練,其中,Res2Net模型對(duì)蛋雞核心體溫估測(cè)擬合效果最好,在測(cè)試集上估測(cè)的決定系數(shù)R2為0.9565、調(diào)整后決定系數(shù)R2adj為0.95631,均高于其他回歸模型;為進(jìn)一步提高預(yù)測(cè)精度,在Res2Net50回歸模型的Bottle2block結(jié)構(gòu)之后分別插入SE(Squeeze-and-excitation)模塊、CBAM(Convolutional block attention module)模塊、CA(Coordinate attention)模塊、ECA(Efficient channel attention)模塊,其中利用CA模塊改進(jìn)后的算法在測(cè)試集上的R2為0.97364、R2adj為0.97352,均高于其他改進(jìn)方法;利用目標(biāo)檢測(cè)網(wǎng)絡(luò)和回歸網(wǎng)絡(luò)搭建蛋雞核心體溫估測(cè)模型,對(duì)9只蛋雞進(jìn)行體溫估測(cè)試驗(yàn),結(jié)果顯示ROI均能完整找出,且估測(cè)體溫平均絕對(duì)誤差(Mean absolute error, MAE)為0.153℃。因此,本研究提出的目標(biāo)檢測(cè)+深度神經(jīng)網(wǎng)絡(luò)模型為紅外熱圖像下蛋雞核心溫度預(yù)測(cè)提供了較好的自動(dòng)化檢測(cè)方法。

    Abstract:

    Core body temperature (CBT) measurement of laying hens is very complex under cage breeding conditions. Meanwhile, traditional measurement methods also require handing the hens, can be stressful. Infrared thermography is an alternative means for assessing hens core temperature. A method was proposed for estimating the CBT of laying hens using infrared thermography and deep learning. A total of 10994 infrared thermal images and corresponding CBT were collected through 172 hens. The hens facial were selected as region of interest (ROI). The YOLO v8s object detection algorithm was employed to automatically identify the ROI within the images. Additionally, the modified Res2Net50 network was used for regression training between ROI images and CBT values. Then the above two algorithms were combined to directly estimate the CBT of laying hens using infrared thermal images. Comparative experiments were conducted with four object detection algorithms (YOLO v4s, YOLO v5s, YOLO v7, YOLOX-s), and the results indicated that YOLO v8s achieved superior precision (99.38%), mAP(99.9%), and recall(99.87%), compared with the other algorithms. Furthermore, seven algorithms (MobileNetV3, GhostNet, ShuffleNetV2, RegNet, ConvNeXt, Res2Net, MobileVIT) were compared with the modified Res2Net, and the results demonstrated that the modified Res2Net exhibited a higher coefficient of determination (R2) of 0.97364 and adjusted coefficient of determination (R2adj) of 0.97352 on the test images, surpassing the other algorithms. Finally, CBT estimation experiments were conducted by using the YOLO v8s-Res2Net50 algorithm. Nine layers were randomly selected, and their infrared thermal images were input into the algorithm network. The results showed that the ROI could be fully identified, and the mean absolute error (MAE) of estimating CBT was 0.153℃. Thus the proposed deep learning model for CBT estimation can offer an effective automated detection method for assessing CBT in laying hens.

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嚴(yán)煜,盛哲雅,谷月,衡一帆,周昊博,王樹(shù)才.非接觸式籠養(yǎng)蛋雞核心體溫檢測(cè)方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2024,55(8):312-321. YAN Yu, SHENG Zheya, GU Yue, HENG Yifan, ZHOU Haobo, WANG Shucai. Non-contact Core Body Temperature Detection Method for Caged Laying Hens[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(8):312-321.

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  • 收稿日期:2023-11-16
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  • 在線發(fā)布日期: 2024-08-10
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