DualCam: A Novel Benchmark Dataset for Fine-Grained Real-Time Traffic Light Detection

Traffic light detection is essential for self-driving cars to navigate safely in urban areas. Publicly available traffic light datasets are inadequate for the development of algorithms for detecting distant traffic lights that provide important navigation information. We introduce a novel bench-mark traffic light dataset captured using a synchronized pair of narrow-angle and wide-angle cameras covering urban and semi-urban roads. We provide 1032 images for training and 813 synchronized image pairs for testing. Additionally, we provide synchronized test video pairs for qualitative analysis. The dataset includes images of resolution
$1920\times 1080$
covering 10 different classes. Furthermore, we propose a post-processing algorithm for combining outputs from the two cameras. Results show that our technique can strike a balance between speed and accuracy, compared to the conventional approach of using a single camera frame. The dataset and algorithm are available at https://github.com/harinduravin/DualCam