Traffic light detection algorithm based on improved YOLOXs

Abstract The detection and recognition of traffic lights play a vital role in driverless and assisted driving technology, given the rapid advancements in autonomous driving. Vehicles can receive crucial information about traffic signals by precisely locating and identifying traffic lights to ensure safety. To make the traffic light detection model more lightweight and improve the detection speed, this paper proposes an improved YOLOXs traffic light detection algorithm. Firstly, the Ghost module is used to enhance the backbone model of YOLOXs in lightweight, reduce the parameters quantity of the network, and enhance the detection speed. Then, the CBAM module is added to the YOLOX neck model to direct the model’s focus toward the target area and ensure the accuracy of traffic light detection. Many experiments on the S2TLD public datasets display that the enhanced model has fewer parameters and achieves accelerated detection speed, up to 112 FPS, which can achieve real-time detection effects. At the same time, in contrast to the primary network, the accuracy of the enhanced network is improved by 1%, reaching 97%. The recall rate increased by 1.2% to 96.2%; average accuracy increased by 0.3% to 97.1%.