A Pedestrian Detection Case Study for a Traffic Light Controller
摘要
A pedestrian detection system in a traffic light controller is studied. The system is based on Deep Neural Networks (DNNs). We explore several network architectures and hardware platforms to identify the most suitable solution under the given constraints of latency, cost, and precision. Specifically, we study altogether 13 networks from the MobileNet, Yolo, ResNet, and EfficientDet families and 6 platforms based on Nvidia and Intel platforms, conducting 383 experiments. We find that several network-platform combinations meet the given requirements of maximum 100 ms inference latency and 0.9 mean average precision. The most promising are Yolo v5 networks on Nvidia Jetson TX2 and IntelNUC GPU hardware.