Traffic Object Detection System Based on YOLOv5 for V2V Communications

The deep learning techniques on the base of YOLOv5 architecture have been shown to make a traffic objects detection system for V2V communications to ensure traffic safety and traffic flows prediction. According to the detection data the detector allows to estimate the traffic object speed and the coordinates of movement. To improve the accuracy of calculations, the distortion correction algorithm (in geometric optics), the Chang method and the Kalman filter are used. To set the detector to work in online operation mode, an algorithm has been developed that receives video stream from open city surveillance cameras with the processing of a m3u8 playlist. The developed detector can be used on "connected" vehicles to assess the situation on the roads and ensure safety traffic.