论文标题
DualCam：一种新颖的基准数据集，用于细度实时交通灯检测
DualCam: A Novel Benchmark Dataset for Fine-grained Real-time Traffic Light Detection
论文作者
论文摘要
交通灯检测对于自动驾驶汽车在城市地区安全导航至关重要。公开可用的交通信号灯数据集不足以开发用于检测提供重要导航信息的遥远交通信号灯的算法。我们介绍了一个新颖的基准交通灯数据集，该数据集使用一对涵盖城市和半城市道路的一对窄角和广角摄像机捕获。我们提供1032张图像用于训练，并提供813个同步图像对进行测试。此外，我们提供同步视频对进行定性分析。该数据集包括第1920 $ \ times $ 1080的图像，覆盖10个不同类别。此外，我们提出了一种用于结合两个相机输出的后处理算法。结果表明，与使用单个相机框架的传统方法相比，我们的技术可以在速度和准确性之间取得平衡。
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.
