Design of lightweight pedestrian detection network in railway scenes

Abstract

The deployment of many advanced pedestrian detection applications is largely hindered by the high computational cost of deep convolutional neural networks (CNNs). In this paper, we propose a two-step pruning method to design a lightweight pedestrian detection network in railway scenes. The first step is feature pyramid network (FPN) pruning, which utilizes the characteristic of pedestrian in railway scenes and the FPN structure in YOLOv3. The second step is regular channel pruning, which utilizes network slimming knowledge and is an accelerator-friendly pruning strategy. Our two-step pruning method gives about 88% reduction in parameters and about 74% reduction in computing complexity with comparable detection accuracy.