基于YOLO v3与图结构模型的群养猪只头尾辨别方法

Head and Tail Identification Method for Group-housed Pigs Based on YOLO v3 and Pictorial Structure Model

在利用视频监控技术对群养猪只进行自动行为监测时,对猪只准确定位并辨别其头尾位置对提高监测水平至关重要,基于此提出一种基于YOLO v3(You only look once v3)模型与图结构模型(Pictorial structure models)的猪只头尾辨别方法。首先,利用基于深度卷积神经网络的YOLO v3目标检测模型,训练猪只整体及其头部和尾部3类目标的检测器,从而在输入图像中获得猪只整体及头尾部所有的检测结果;然后,引入图结构模型,描述猪只的头尾结构特征,对每个猪只整体检测矩形框内的头尾部位组合计算匹配得分,选择最优的部位组合方式;对部分部位漏检的情况,采取阈值分割与前景椭圆拟合的方法,根据椭圆长轴推理出缺失部位。在实际猪场环境下,通过俯拍获得猪舍监控视频,建立了图像数据集,并进行了检测实验。实验结果表明,与直接利用YOLO v3模型相比,本文方法对头尾定位的精确率和召回率均有一定提高。本文方法对猪只头尾辨别精确率达到96.22%,与其他方法相比具有明显优势。

For automatic behavior monitoring of group-housed pigs in video surveillance,pig head/tail identification has important significance to improve the level of behavior recognition.A head-tail recognition algorithm was proposed based on YOLO v3(You only look once v3)and pictorial structure models.Firstly,the object detectors of three categories,i.e.,pigs,heads and tails,were trained with YOLO v3,which was a general object detection model based on deep convolutional neural networks.In this way,bounding boxes of pigs,heads and tails can be detected from the input image.Next,pictorial structure models were introduced to describe structural characteristics of heads and tails for pigs.For each detected bounding box of pigs,scores of all possible head-tail combinations were computed with the established pictorial structure model to choose the optimal part configuration.When a head or tail was missed in the pig bounding box,a part inference method based on threshold segmentation was utilized to estimate the missing part according to the major axis of the fitted ellipse.In experiments,an image dataset was constructed from a top-view surveillance video of group-housed pigs.Experimental results demonstrated that via the proposed method,the precision and recall of part localization were improved compared with results of YOLO v3.Moreover,the head/tail identification accuracy reached 96.22%,which obviously outperformed other methods based on intersection of bounding boxes and generalized Hough clustering.As a result,the proposed method can effectively detect pigs and distinguish their heads/tails in images of group-housed pigs without excessive limitations on environments.