基于改进YOLO v5s模型的奶山羊乳房区域热红外图像检测方法
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国家重点研发计划项目(2023YFD1301800)和国家自然科学基金项目(32272931)


Thermal Infrared Image Detection Method of Dairy Goat Breast Region Based on Improved YOLO v5s Model
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    摘要:

    奶山羊乳房区域的准确提取是奶山羊非侵入式体温检测的关键,但受乳房区域遮挡及热红外图像分辨率不高等因素影响,其检测精度尚待进一步提升。基于热红外成像技术,提出了一种基于改进YOLO v5s的奶山羊乳房关键部位检测方法。通过将原模型Backbone网络的部分卷积模块替换为ShuffleNetV2结构,以达到降低网络部署和训练过程中的参数量、实现轻量化网络设计的目的。通过在Neck网络检测头(Head)前端引入CBAM注意力机制,以达到在降低网络复杂程度的同时保证奶山羊乳房区域检测精度的目的。本研究采集了包含完整信息、残缺信息和边缘模糊的孕期奶山羊乳房红外图像4611幅,并在部位标注后进行模型训练。经测试,模型精确率为93.7%,召回率为86.1%,平均精度均值为92.4%,参数量为8×105,浮点运算量为1.9×109。与YOLO v5n、YOLO v5s、YOLO v7-tiny、YOLO v7、YOLO v8n和YOLO v8s目标检测网络相比,网络的精确率分别提高1.9、1.2、1.6、4.3、3.5、2.7个百分点,召回率提高3.4、5.0、0.1、2.6、0.9、1.5个百分点,参数量降低1.1×106、6.2×106、5.2×106、3.6×107、2.4×106和1.0×107,浮点运算量降低2.6×109、1.4×1010、1.1×1010、1.0×1011、6.8×109和2.7×1010。试验结果表明,本研究所提出的网络可以实现奶山羊乳房关键部位的精确检测,且在不损失检测精度的基础上显著降低网络的参数量,有利于网络在不同环境下的部署和使用,可为奶山羊非接触式体温监测系统设计提供借鉴。

    Abstract:

    Accurate extraction of the udder region of dairy goats was the key to realize non-invasive temperature detection of dairy goats. Due to the occlusion of breast area and the low quality of thermal infrared image, the detection accuracy needs to be further improved. Based on thermal infrared imaging technology, an improved YOLO v5s based detection method for key parts of milk goat udder was proposed. By replacing some convolutional modules of Backbone network in the original model with ShuffleNetV2 structure, the number of parameters in network deployment and training process was reduced, and the purpose of lightweight network design was realized. By introducing CBAM attention mechanism into the head of the Neck network detection head, the complexity of the network has been reduced and the detection accuracy of the breast region of dairy goats was ensured. Totally 4611 infrared images of breast of pregnant dairy goats containing complete information, incomplete information and blurred edges were collected, and the model was trained after location labeling. After testing, the accuracy of the model was 93.7%, the recall rate was 86.1%, the mean average precision was 92.4%, the number of parameters was 8×105, and the floating point computation was 1.9×109. Compared with the YOLO v5n,YOLO v5s,YOLO v7-tiny,YOLO v7,YOLO v8n and YOLO v8s target detection network, the accuracy of this network was increased by 1.9 percentage points,1.2 percentage points,1.6 percentage points,4.3 percentage points,3.5 percentage points and 2.7 percentage points, the recall rate was increased by 3.4 percentage points,5.0 percentage points,0.1 percentage points,2.6 percentage points,0.9 percentage points and 1.5 percentage points, the number of parameters was decreased by 1.1×106,6.2×106,5.2×106,3.6×107,2.4×106 and 1.0×107, and floating-point calculations were reduced by 2.6×109,1.4×1010,1.1×1010,1.0×1011,6.8×109 and 2.7×1010, respectively. It met the detection requirements of the key parts of milk goat udder, and significantly reduced the number of parameters of the network without losing the detection accuracy, which was conducive to the deployment and use of the network in different environments, and had reference significance for the design of non-contact temperature monitoring system for milk goat body temperature.

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温毓晨,赵永杰,蒲六如,邓洪兴,张姝瑾,宋怀波.基于改进YOLO v5s模型的奶山羊乳房区域热红外图像检测方法[J].农业机械学报,2024,55(6):237-245. WEN Yuchen, ZHAO Yongjie, PU Liuru, DENG Hongxing, ZHANG Shujin, SONG Huaibo. Thermal Infrared Image Detection Method of Dairy Goat Breast Region Based on Improved YOLO v5s Model[J]. Transactions of the Chinese Society for Agricultural Machinery,2024,55(6):237-245.

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  • 收稿日期:2023-10-26
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  • 在线发布日期: 2024-06-10
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