Sheep Counting Method Based on Multiscale Module Deep Neural Network

Abstract

Due to the uneven distribution and large scale change of sheep in the pasture, it is not conducive to the counting and statistics of sheep in animal husbandry. The traditional target counting algorithm has low counting accuracy in the field of animal husbandry, and there are fewer sheep data sets for research. To solve these problems, the data set of sheep density estimation was established, and a method of grassland sheep number estimation based on multi-scale residual visual information fusion Network (MRVIFNet) was proposed. This method extracts multi-scale features of sheep targets by using multiple parallel hole convolutions with different hole rates, and designs a depth neural network that is more suitable for live counting of sheep, so as to reduce the grid effect caused by hole convolution and better adapt to multi-scale changes of sheep. In the sheep density data set, the method obtained the lowest mean absolute error (MAE) and root mean square error (RMSE). In addition, a convolutional neural network model based on view branch sharing is also studied. Compared with the five popular methods, this method can achieve better performance. It is applied to solve the problem of pedestrian scale change and chaotic distribution in complex scenes; The performance of this method is better than that of comparison method, and the application results in actual scenarios verify the effectiveness of this method.