DualCam: A Novel Benchmark Dataset for Fine-grained Real-time Traffic Light Detection
This work addresses a critical safety issue for autonomous vehicles by providing a dataset for fine-grained traffic light detection, though it is incremental as it builds on existing detection methods with a new dataset and algorithm.
The authors tackled the problem of detecting distant traffic lights for self-driving cars by introducing a new benchmark dataset captured with synchronized narrow-angle and wide-angle cameras, and their post-processing algorithm achieved a balance between speed and accuracy compared to single-camera methods.
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 benchmark 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 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.