SYOLO: An Efficient Pedestrian Detection

Abstract

Pedestrian detection is an important branch of object detection. It plays a vital role in many fields such as intelligent monitoring systems. The premise of the pedestrian recognition algorithm in the application of the industrial scene is accurate pedestrian detection. Our paper proposes a model to solve real-time pedestrian detection with high accuracy base on YOLO v3. We provide a method to select the size and number of anchor boxes for predicting bounding boxes accurately. Then we use a modified shuffle unit to lightweight the backbone of YOLO v3, which reduces the 67.3% FLOPs and 65.1% parameters. We train and validate our model on CrowdHuman detection dataset, SYOLO gets 62.7 mAP for face and 62.0mAP person with 0.748 average IOU. Our network processes images in real-time at 185.8 FPS for network and 12.3 FPS for the entire model on CrowdHuman.