Embedded System Real-Time Vehicle Detection based on Improved YOLO Network

Abstract

Real-time detection of the pavement environment is an important part of autonomous driving technology. This paper presents a real-time vehicle detection system based on embedded devices. Based on the existing yolov3-tiny neural network structure, this paper proposes a new neural network structure - the YOLO v3-live. Tailoring the network layer structure of YOLOv3-tiny, and quantify the network parameters in the network. Reducing the complexity of computing in embedded devices, making the proposed neural network structure more suitable for embedded devices. The new structure is tested, before quantization the YOLO v3-live's detection precision can achieve 87.79% mAP on the test set, after quantization of network parameters can achieve 69.79% mAP and detection speed can achieve 28 FPS.