An LED Detection and Recognition Method Based on Deep Learning in Vehicle Optical Camera Communication
Xu Sun, Wenxiao Shi, Qing Cheng, Wei Liu, Zhuo Wang, Jiadong Zhang
Abstract
In the Vehicle to Vehicle (V2V) communication based on Optical Camera Communication (OCC), optical signals are transmitted using LED arrays and received employing cameras. In a complex scene, how to accurately detect and recognize LEDs in real time remains a problem. To solve this problem, this paper designs an end-to-end network based on You Only Look Once version 5 (YOLOv5) object detection model, which can precisely detect the LED array position in real time and alleviate motion blur simultaneously. Further, we propose an LED segmentation recognition method, which is beneficial to more reliable LED status recognition. It allows more light sources to be used for communication, which can effectively improve data rate in the vehicle OCC system. The effectiveness of our method is demonstrated by theoretical analysis and experiments in real traffic scenes. Our code is available at https://github.com/cq100/D2Net.