基于改进FasterR-CNN识别深度视频图像哺乳母猪姿态

摘要

猪舍场景下,昼夜交替光线变化、热灯光照影响,及仔猪与母猪的粘连等因素,给全天候哺乳母猪姿态自动识别带来很大困难.该文以深度视频图像为数据源,提出基于改进Faster R-CNN的哺乳母猪姿态识别算法.将残差结构引入ZF网络,设计ZF-D2R网络,以提高识别精度并保持实时性;将Center Loss监督信号引入Faster R-CNN训练中,以增强类内特征的内聚性,提升识别精度.对28栏猪的视频图像抽取站立、坐立、俯卧、腹卧和侧卧5类姿态共计7541张图像作为训练集,另取5类姿态的5000张图像作为测试集.该文提出的改进模型在测试集上对哺乳母猪的站立、坐立、俯卧、腹卧和侧卧5类姿态的识别平均准确率分别达到96.73%、94.62%、86.28%、89.57%和99.04%,5类姿态的平均准确率均值达到93.25%.在识别精度上,比ZF网络和层数更深的VGG16网络的平均准确率均值分别提高了3.86和1.24个百分点.识别速度为0.058 s/帧,比VGG16网络速度提高了0.034 s.该文方法在提高识别精度的同时保证了实时性,可为全天候母猪行为识别提供技术参考.%The maternal behaviors reflect the health and welfare of the sows, which directly affect the economic benefit of the pig farm. Computer vision provides an effective, low-cost and non-contact method for monitoring the behavior of animal for precision farming. Under the scene of piggery, it is a challenge for 24-hour automatic recognition of lactating sow postures due to the daily illumination variations, influence of heat lamp, and adhesion between piglets and sows. This paper proposed an automatic recognition algorithm of lactating sow postures based on improved Faster R-CNN (convolutional neural network) using depth video images. To improve the recognition accuracy and satisfy the real-time need, we designed a ZF-D2R (ZF with deeper layers and 2 residual learning frameworks) network by introducing residual learning frameworks into ZF network. First, 3 convolutional layers were added in the ZF network to design ZF-D (ZF with deeper layers). Then, in ZF-D network, shortcut connections were used to form 2 residual learning frameworks. The whole network made up the ZF-D2R network. Moreover, the Center Loss was introduced to Fast R-CNN detector to construct a joint classification loss function. With the joint supervision signals of F-SoftmaxLoss and Center Loss in Fast R-CNN detector, a robust model was trained to obtain the deep feature representations with the 2 key learning objectives, which led to intra-class compactness and inter-class dispersion as much as possible. So, the joint supervision of F-SoftmaxLoss and Center Loss could reduce recognition errors caused by the similar features between different postures. By taking ZF-D2R as basic net and adding the Center Loss to Fast R-CNN detector, the improved Faster R-CNN was built. Experiments to obtain the actual data set of lactating sow posture from the depth video of sows in the 28 pens were performed. The data set included 2 451 standing images, 2 461 sitting images, 2 488 sternal recumbency images, 2 519 ventral recumbency images and 2658 lateral recumbency images. And 5 000 images were randomly chosen as the testing set. The rest of the images were used as training set. To enhance the diversity of training data, dataset augmentation including rotating and mirroring was employed. Based on the Caffe deep learning framework, our improved Faster R-CNN was trained with end-to-end approximate joint methods. By adding 2 residual learning frameworks to ZF-D, the ZF-D2R model improved the MAP (mean of average precision) by 1.28 percentage points. After introducing the Center Loss supervision signal, the MAP of the optimal model reached 93.25%, obtaining an increase of 1.3 percentage points, and the MAP of the method proposed achieved 93.25%. And APs (average precisions) of the 5 classes of postures i.e. standing, sitting, sternal recumbency, ventral recumbency and lateral recumbency were 96.73%, 94.62%, 86.28%, 89.57% and 99.04%, respectively. The MAP of our approach was 3.86 and 1.24 percentage points higher than that of Faster R-CNN based on ZF basic net and Faster RCNN based on the deeper VGG16 basic net, respectively. Our method processed images at a speed of 0.058 s per frame, 0.034 s faster than Faster R-CNN based on VGG16. Our proposed method could improve the recognition accuracy and simultaneously ensure the real-time performance. Compared with DPM (deformable part model) detector plus CNN posture classifier, the MAP of the end-to-end recognition method proposed in this paper was increased by 37.87 percentage points, and the speed was raised by 0.855 s per frame. Our method can be used for the 24-hour recognition of sow behaviors and lays the foundation for the analysis of sow dynamic behavior by video.