A Deep Learning Framework for Robust and Real-Time Taillight Detection Under Various Road Conditions

Abstract

In this paper, we present a deep learning model for high-accuracy, high-speed detection of vehicle taillights in traffic. The model consists of three major modules: the lane detector, the car detector, and the taillight detector. Unlike most previously proposed algorithms where hand-coded schemes are used, we have adopted a data-driven approach. This data-driven scheme was implemented in both the car and taillight detection modules. First, we used an intricately designed lane detection module, then we adopted the Recurrent Rolling Convolution (RRC) architecture and tracking mechanism for detecting car boundaries. Subsequently, we used the same RRC architecture to extract the taillight regions of the detected cars. The lane detection and car detection modules improve both the speed and detection rate of the final taillight detection. The robustness of our model was verified using datasets from Sungkyunkwan University (SKKU) as well as the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI). Our model works well even in hostile conditions. It achieves detection rates as high as 99% in testing with the SKKU dataset. When using the KITTI 2D Object dataset, the model achieves a taillight detection rate of 86%. The model achieves 100% taillight detection rate on a certain, small subset of the KITTI Tracking dataset.