In order to solve the problem of intelligent management of cattle numbers in the pasture, a dataset of cattle density estimation was established, and a multi-scale residual cattle density estimation network was proposed to solve the problems of uneven distribution of cattle and large scale variations caused by perspective changes in the same image. Multi-scale features are extracted by multiple parallel dilated convolutions with different dilation rates. Meanwhile, aiming at the “grid effect” caused by the use of dilated convolution, the residual structure is combined with a small dilation rate convolution to eliminate the influence of the “grid effect”. Experiments were carried out on the cattle dataset and dense population dataset, respectively. The experimental results show that the proposed multi-scale residual cattle density estimation network achieves the lowest mean absolute error (MAE) and means square error (RMSE) on the cattle dataset compared with other density estimation methods. In ShanghaiTech, a dense population dataset, the density estimation results of the multi-scale residual network are also optimal or suboptimal in MAE and RMSE.