Parallel Scale Cropping Deep Convolutional Neural Network Model

As the excessive parameters of parallel convolutional neural network (PCNN) and the high cost of model training time, this paper proposes a parallel scale cropping convolutional neural network (PSC-CNN). The PSC-CNN algorithm is that obtains an input for one path (Path A) of the parallel convolutional neural network and an output of the feature extraction layer of the path, and then to get a new size image as input of the other path (Path B) network through the Crop layer. In this way, the input image of Path A undergoes a random cropping in the data layer, and Path B undergoes two cropping operations, which increases the data diversity and improves the learning ability. The algorithm is based on AlexNet, and the corresponding PCNN, PSC-CNN models are designed respectively. Experiments are carried out on the datasets Caltech101 and Caltech256. Experimental results show that compared with the original PCNN, the improved algorithm proposed in this paper effectively improves the classification accuracy and shortens the training time.