Affiliation:
1. College of Information Science and Engineering, Shanxi Agricultural University/China
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.
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering,Food Science
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