Human Segmentation Based on Compressed Deep Convolutional Neural Network

Abstract

Most semantic segmentation models based on deep convolutional neural network (CNN) typically require a large number of weight parameters, high hardware resources for storage and computation. Moreover, redesigning a compact network suffers from some training problems, such as under-fitting. A human segmentation algorithm is proposed based on compressed deep CNN to optimize the convolution layers and filters. PSPNet-50 is fine-tuned on the human segmentation dataset to obtain the human segmentation model with higher accuracy. Then the convolutional-layer level pruning and corresponding structure optimization are performed so that the parameters of the model are substantially reduced. Finally, the two-stage global filter-level pruning strategy is used. Compared with the method of layer by layer pruning and retraining, our strategy not only reduces parameters of the model and saves the time of retraining, but also keeps the high IoU (Intersection over Union) accuracy. In addition, by adding auxiliary losses in the network during training CNN, the supervised training of the network is improved, and IoU is further increased. Compared to the model before compression, the sufficient experiments show that the parameter number, computation cost, memory consumption, and parameter storage are decreased by 1/7.5, 5.6/6.6, 0.7/1, 6.5/7.5, respectively, while the segmentation speed is accelerated by 2.4 times, and IoU on test set reaches 93.2%.