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非接触式笼养蛋鸡核心体温检测方法

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针对笼养条件下蛋鸡核心温度测量工作效率低下的问题,提出了一种利用红外热图像结合深度学习的蛋鸡核心温度检测方法.首先通过采集172只蛋鸡的10 994幅红外热图像制作数据集,利用目标检测网络YOLO v8s提取作为感兴趣区域(Region of interest,ROI)的鸡脸图像;再利用改进的深度卷积神经网络对提取的蛋鸡ROI图像以及实时采集的蛋鸡泄殖腔温度进行回归预测.实验显示,目标检测算法的检测准确率达到99.38%,平均精度均值达到99.9%,召回率达到99.87%,3项评价指标均高于YOLO v4s、YOLO v5s、YOLO v7、YOLOX-s目标检测算法;在深度卷积神经网络算法上,同时将 MobileNetV3、GhostNet、ShuffleNetV2、RegNet、ConvNeXt、Res2Net 以及MobileVIT共7种分类模型修改为回归模型,利用蛋鸡ROI图像进行训练,其中,Res2Net模型对蛋鸡核心体温估测拟合效果最好,在测试集上估测的决定系数R2为0.956 5、调整后决定系数R2adj为0.956 31,均高于其他回归模型;为进一步提高预测精度,在Res2Net50回归模型的Bottle2block结构之后分别插入SE(Squeeze-and-excitation)模块、CBAM(Convolutional block attention module)模块、CA(Coordinate attention)模块、ECA(Efficient channel attention)模块,其中利用CA模块改进后的算法在测试集上的R2为0.973 64、R2adj为0.973 52,均高于其他改进方法;利用目标检测网络和回归网络搭建蛋鸡核心体温估测模型,对9只蛋鸡进行体温估测试验,结果显示ROI均能完整找出,且估测体温平均绝对误差(Mean absolute error,MAE)为0.153℃.因此,本研究提出的目标检测+深度神经网络模型为红外热图像下蛋鸡核心温度预测提供了较好的自动化检测方法.
Non-contact Core Body Temperature Detection Method for Caged Laying Hens
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 10 994 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 0.973 64 and adjusted coefficient of determination(R2dj)of 0.973 52 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.

caged laying henscore body temperatureYOLO v8s-Res2Net50infrared thermal image

严煜、盛哲雅、谷月、衡一帆、周昊博、王树才

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华中农业大学工学院,武汉 430070

华中农业大学动物科学技术学院、动物医学院,武汉 430070

农业动物遗传育种与繁殖教育部重点实验室,武汉 430070

笼养蛋鸡 核心体温 YOLO v8s-Res2Net50 红外热成像

2024

农业机械学报
中国农业机械学会 中国农业机械化科学研究院

农业机械学报

CSTPCD北大核心
影响因子:1.904
ISSN:1000-1298
年,卷(期):2024.55(8)