TL-Detector: Lightweight Based Real-Time Traffic Light Detection Model for Intelligent Vehicles
Zikai Yao, Qiang Liu, Qian Xie, Qing Li
Abstract
With the leap-forward development of intelligent driving, traffic light detection with high accuracy and high speed is important for intelligent transportation systems to ensure safety. However, existing methods encounter difficulties in balancing the detection speed and accuracy. This paper aims to present a flexible and robust lightweight model TL (Traffic Light)-Detector for real-time detection of traffic light. The model is composed of three parts. An enhanced backbone network combined with G-module is proposed to generate abundant information and reduce computational load, and the coordinate attention mechanism is introduced to focus on location features thereby strengthening the feature extract ability. The lightweight neck promotes multi-scale feature information aggregation for feature fusion to build a lightweight feature fusion network. The lightweight detection head adopts anchor-free mechanism to eliminate the hyperparameters related to the anchor. The dataset TL2022 of traffic light is built based on real traffic sceneries. Experimental results show that TL-Detector has the best comprehensive performance. TL-Detector achieves a detection speed of 277 FPS and a precision of 73.24% with only 0.72 GFLOPs. The experiments on LaRA (La Route Automatisee) public traffic light dataset show the excellent generalization ability. It indicates that TL-Detector can effectively achieve accurate and real-time detection for traffic lights.