A Lightweight Convolutional Network Based on Pruning Algorithm for YOLO

With the rapid development of deep learning, neural network models have become increasingly complicated, leading to larger storage space requirements and slower reasoning speed. These factors make it difficult to be deployed on resource-limited platforms. To alleviate this problem, network pruning, an effective model compression method, is commonly performed in a deep neural network. However, traditional pruning methods simply set redundant weights to zero, thus failing to achieve the acceleration effect. In this paper, a channel-wise model scaling method is proposed to reduce the model size and speed up reasoning by structurally removing the redundant filters in convolutional layers. To make the residual block more sparse, we develop a pruning method for residual cells. Experimental results on the YOLOv3 detector show that our proposed approach achieves a 70.6% parameter compression ratio without compromising accuracy.