An innovative traffic light recognition method using vehicular ad-hoc networks

Abstract

Car congestion is a pressing issue for everyone on the planet. Car congestion can be caused by accidents, traffic lights, rapid accelerations, deceleration, and hesitation of drivers, as well as a small low-carrying capacity road without bridges. Increasing road width and constructing roundabouts and bridges are solutions to car congestion, but the cost is significant. TLR (traffic light recognition) reduces accidents and traffic congestion caused by traffic lights (TLs). Image processing with convolutional neural network (CNN) lakes dealing with harsh weather. A semi-automatic annotation for traffic light detection employs a global navigation satellite system, raising the cost of automobiles. Data was not collected in harsh conditions, and tracking was not supported. Integrated channel feature tracking (ICFT) combines detection and tracking, but it does not support sharing information with neighbors. This study used vehicular ad-hoc networks (VANETs) for VANET traffic light recognition (VTLR). Information exchange as well as monitoring of the TL status, time remaining before a change, and recommended speeds are supported. Based on testing, it has been determined that VTLR performs better than semi-automatic annotation, image processing with CNN, and ICFT in terms of delay, success ratio, and the number of detections per second.

FPGA-Based Real-Time Object Detection and Classification System Using YOLO for Edge Computing

“…In addition, Fernando et al [14] and Huy et al [15] have also demonstrated traffic light detection and classification using YOLO. Furthermore, various methods based on Lightweight [16], PID Controller [17], Salience-Sensitive Loss [18], vehicular ad-hoc networks [19], support vector machine [20], and Adaptive Background Suppression Filter [21] have been reported for traffic light detection and classification.…”