Authors

Abstract

Due to the complex environment of pig farms and the diversity of pig behaviors, the existing methods based on deep learning cannot meet the requirements of high accuracy and real-time detection of pig eating behavior. In this paper, a SE-YOLOX model for detecting pig diet and drinking behaviors was designed. In this model, a Squeeze-and-Excitation (SE) attention module is introduced between the neck layer and the prediction layer of YOLOX, and the input feature map is compressed into a vector through global average pooling operation, and then mapped to a smaller vector through a fully connected layer. A sigmoid function is also used to compress each element in this vector to between 0 and 1 and multiply it with the original input feature map to get the weighted feature map. Through SE attention mechanism, the model can learn the importance of each channel adaptively, thus improving the detection accuracy. The experimental results show that the mean Average Prediction (mAP) of the SE-YOLOX model is 88.03%, which is higher than 13.11% of the original YOLOX model. SE-YOLOX can ensure real-time performance, it also can effectively improve the accuracy of pig diet and drinking water behavior detection.

Abstract in Chinese

由于养猪场环境的复杂和猪行为的多样性，现有的基于深度学习的方法难以满足猪食用行为检测的高精度和实时性要求。本文提出了一种基于注意机制的SE-YOLOX模型，该模型在YOLOX的主干与注意机制相连接，可以使浓度梯度效应在网络中深入，从而提高检测精度。实验结果表明，SE-YOLOX模型的mAP0.5:0.95为88.03%，高于YOLOX的13.11%。比其他主流机型高19.52% ~ 42.14%。SE-YOLOX在保证实时性的同时，能有效提高猪饲粮行为检测的准确性，对模型的稳定性有突出的影响。