Yu Longhui, Guo Jianjun, Pu Yuhai, Cen Honglei, Li Jingbin, Liu Shuangyin, Nie Jing, Ge Jianbing, Yang Shuo, Zhao Hangxing, Xu Yalei, Wu Jianglin, Wang Kang
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, China.
Xinjiang Production and Construction Corps Key Laboratory of Modern Agricultural Machinery, Shihezi 832003, China.
Animals (Basel). 2023 Jan 26;13(3):413. doi: 10.3390/ani13030413.
There are some problems with estrus detection in ewes in large-scale meat sheep farming: mainly, the manual detection method is labor-intensive and the contact sensor detection method causes stress reactions in ewes. To solve the abovementioned problems, we proposed a multi-objective detection layer neural network-based method for ewe estrus crawling behavior recognition. The approach we proposed has four main parts. Firstly, to address the problem of mismatch between our constructed ewe estrus dataset and the YOLO v3 anchor box size, we propose to obtain a new anchor box size by clustering the ewe estrus dataset using the K-means++ algorithm. Secondly, to address the problem of low model recognition precision caused by small imaging of distant ewes in the dataset, we added a 104 × 104 target detection layer, making the total target detection layer reach four layers, strengthening the model's ability to learn shallow information and improving the model's ability to detect small targets. Then, we added residual units to the residual structure of the model, so that the deep feature information of the model is not easily lost and further fused with the shallow feature information to speed up the training of the model. Finally, we maintain the aspect ratio of the images in the data-loading module of the model to reduce the distortion of the image information and increase the precision of the model. The experimental results show that our proposed model has 98.56% recognition precision, while recall was 98.04%, F1 value was 98%, mAP was 99.78%, FPS was 41 f/s, and model size was 276 M, which can meet the accurate and real-time recognition of ewe estrus behavior in large-scale meat sheep farming.
在大规模肉羊养殖中，母羊发情检测存在一些问题：主要是人工检测方法劳动强度大，而接触式传感器检测方法会使母羊产生应激反应。为了解决上述问题，我们提出了一种基于多目标检测层神经网络的母羊发情爬跨行为识别方法。我们提出的方法主要有四个部分。首先，为了解决我们构建的母羊发情数据集与YOLO v3锚框大小不匹配的问题，我们建议使用K-means++算法对母羊发情数据集进行聚类，以获得新的锚框大小。其次，为了解决数据集中远处母羊成像较小导致模型识别精度低的问题，我们添加了一个104×104的目标检测层，使总目标检测层达到四层，增强模型学习浅层信息的能力，提高模型检测小目标的能力。然后，我们在模型的残差结构中添加了残差单元，使模型的深度特征信息不易丢失，并进一步与浅层特征信息融合，以加快模型的训练。最后，我们在模型的数据加载模块中保持图像的宽高比，以减少图像信息的失真，提高模型的精度。实验结果表明，我们提出的模型识别精度为98.56%，召回率为98.04%，F1值为98%，平均精度均值为99.78%，每秒帧数为41帧，模型大小为276M，能够满足大规模肉羊养殖中母羊发情行为的准确实时识别。
