段青玲,赵芷青,蒋涛,桂小飞,张宇航.基于SNSS-YOLO v7的肉牛行为识别方法[J].农业机械学报,2023,54(10):266-274,347.
DUAN Qingling,ZHAO Zhiqing,JIANG Tao,GUI Xiaofei,ZHANG Yuhang.Behavior Recognition Method of Beef Cattle Based on SNSS-YOLO v7[J].Transactions of the Chinese Society for Agricultural Machinery,2023,54(10):266-274,347.
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| 基于SNSS-YOLO v7的肉牛行为识别方法 [下载全文] |
| Behavior Recognition Method of Beef Cattle Based on SNSS-YOLO v7 [Download Pdf][in English] |
| 投稿时间：2023-04-14 |
| DOI：10.6041/j.issn.1000-1298.2023.10.026 |
| 中文关键词: 肉牛 行为识别 深度学习 YOLO v7 多目标识别 |
| 基金项目:北京市博士后工作经费项目(2022-ZZ-109)和校企合作项目（202305510810142） |
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| 中文摘要:肉牛活动过程中所表现出的行为是肉牛健康状况的综合体现，实现肉牛行为的快速准确识别，对肉牛疾病防控、自身发育评估和发情监测等具有重要作用。基于机器视觉的行为识别技术因其无损、快速的特点，已应用在畜禽养殖行为识别中，但现有的基于机器视觉的肉牛行为识别方法通常针对单只牛或单独某个行为开展研究，且存在计算量大等问题。针对上述问题，本文提出了一种基于SNSS-YOLO v7（Slim-Neck & Separated and enhancement attention module & Simplified spatial pyramid pooling-fast-YOLO v7）的肉牛行为识别方法。首先在复杂环境下采集肉牛的爬跨、躺卧、探究、站立、运动、舔砥和互斗7种常见行为图像，构建肉牛行为数据集；其次在YOLO v7颈部采用Slim-Neck结构，以减小模型计算量与参数量；然后在头部引入分离和增强注意力模块（Separated and enhancement attention module，SEAM）增强Neck层输出后的检测效果；最后使用SimSPPF（Simplified spatial pyramid pooling-fast）模块替换原YOLO v7的SPPCSPC（Spatial pyramid pooling cross stage partial conv）模块，在增大感受野的同时进一步减少参数量。在自建数据集上测试，本文提出的肉牛行为识别方法的平均精度均值（mAP@0.5）为95.2%，模型内存占用量为39 MB，参数量为1.926×107。与YOLO v7、YOLO v6m、YOLO v5m、YOLOX-S、TPH-YOLO v5、Faster R-CNN相比，模型内存占用量分别减小47.9%、45.4%、7.6%、43.1%、57.8%和92.5%，平均精度均值（mAP@0.5）分别提高1.4、2.2、3.1、13.7、1.9、4.5个百分点，试验结果表明，本文方法能够实现肉牛行为的准确识别，可以部署在计算资源有限的设备上，为实现畜禽养殖智能化提供支持。 |
| DUAN Qingling, ZHAO Zhiqing, JIANG Tao, GUI Xiaofei, ZHANG Yuhang |
| China Agricultural University;Beijing Futong Internet Technology Group Co., Ltd. |
| Key Words:beef cattle behavior recognition deep learning YOLO v7 multi-object recognition |
| Abstract:The behavior of beef cattle in the process of activity is the comprehensive embodiment of the health status of beef cattle. The rapid and accurate recognition of beef cattle behavior plays an important role in the prevention and control of beef cattle diseases, their own development assessment and estrus monitoring. Behavior recognition technology based on machine vision has been applied to behavior recognition of livestock and poultry breeding because of its lossless and fast characteristics. However, the existing behavior recognition methods of beef cattle based on machine vision were usually studied for a single cow or a single behavior, and there were problems such as large amount of calculation. In view of the above problems, a method based on Slim-Neck & Separated and enhancement attention module & Simplified spatial pyramid pooling-fast-YOLO v7 (SNSS-YOLO v7) was proposed. Firstly, seven common behavior images of beef cattle, such as mounting, lying, searching, standing, walking, licking and fighting, were collected in the complex environment to construct a beef cattle behavior dataset. Secondly, the Slim-Neck structure was used in the neck of YOLO v7 to reduce the amount of calculation and parameters of the model. Then, separated and enhancement attention module (SEAM) was introduced into the head to enhance the detection effect after the output of the Neck layer. Finally, the simplified spatial pyramid pooling-fast (SimSPPF) module was used to replace the spatial pyramid pooling cross stage partial conv (SPPCSPC) module of the original YOLO v7, which further reduced the number of parameters while increased the receptive field. Tested on the self built dataset, the mean average precision (mAP@0.5) of the beef cattle behavior recognition method proposed was 95.2%, the model size was 39MB, and the number of parameters was 1.926×107. Compared with YOLO v7, YOLO v6m, YOLO v5m, YOLOX-S, TPH-YOLO v5 and Faster R-CNN, the model size was reduced by 47.9%, 45.4%, 7.6%, 43.1%, 57.8% and 92.5%, respectively. The mean average precision (mAP@0.5) was improved by 1.4 percentage points, 2.2 percentage points, 3.1 percentage points, 13.7 percentage points, 1.9 percentage points, and 4.5 percentage points, respectively. The experimental results showed that the proposed method can achieve accurate recognition of beef cattle behavior, and can be deployed on devices with limited computing resources to provide support for intelligent livestock breeding. |
Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.
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