Real-time Pedestrian Detection Algorithm Based on Improved YOLOv3

Abstract

As a research hotspot in the field of current computer vision, pedestrian detection is widely applied to many fields, such as video surveillance and autonomous driving. However, the accuracy of pedestrian detection under video surveillance is poor, and the miss rate of small target pedestrians is high. In this paper, an improves the YOLOv3 algorithm and a YOLOv3-Multi pedestrian detection model had been proposed. First, referring to the residual structure of DarkNet, the shallow features and deep features had been up-sampled and connected to obtain a multi-scale detection layer. Then, according to different special detection categories, the spatial pyramid pool (SPP) is introduced to strengthen the detection of small targets. The experimental results show that our method improves the average accuracy by 2.54%, 6.43% and 8.99%compared with YOLOv3, SSD and YOLOv2 on the VOC dataset.