Image Recognition Based on Multiscale Pooling Deep Convolution Neural Networks

Depth neural network (DNN) has become a research hotspot in the field of image recognition. Developing a suitable solution to introduce effective operations and layers into DNN model is of great significance to improve the performance of image and video recognition. To achieve this, through making full use of block information of different sizes and scales in the image, a multiscale pooling deep convolution neural network model is designed in this paper. No matter how large the feature map is, multiscale sampling layer will output three fixed-size character matrices. Experimental results demonstrate that this method greatly improves the performance of the current single training image, which is suitable for solving the image generation, style migration, image editing, and other issues. It provides an effective solution for further industrial practice in the fields of medical image, remote sensing, and satellite imaging.