Crowd counting is an important part of crowd analysis, which is of great significance to crowd control and management. The convolutional neural network (CNN) based crowd counting method is widely used to solve the problem of insufficient counting accuracy due to heavy occlusion, background clutters, head scale and perspective changes in crowd scenes. The multi-column convolutional neural network (MCNN) is a CNN-based method for crowd counting, which adapts to head scale variation of crowd scenes by constructing multi-column convolutional neural network composing of three single-column networks corresponding to the convolution kernel with different sizes (large, medium and small). However, as the MCNN network is relatively shallow, its receptive field is also limited, which affects the adaptability to large scale variations. In addition, due to insufficient training data, it is necessary to carry out a pre-training strategies which pre-trains the single-column convolutional neural network individually and combines the cumbersome. In this paper, a crowd counting method based on multi-column dilated convolutional neural network was proposed. Dilated convolution was used to enhance the receptive field of the network, so as to be better adaptive to the head scale variations. The image patches were obtained by randomly clipping from the original training data set images in the process of each iterative training to further expand the training data, while the training could be achieved without tedious pre-training. The experimental results on ShanghaiTech public dataset showed that the accuracy of crowd counting proposed in this paper was better than that of MCNN, which proved that this method is more robust to head scale variations in crowd scenes.