DualCam

DualCam是一个创新的交通灯检测基准数据集，由莫勒图沃大学电子与电信工程系创建。该数据集包含2250张标注图像，使用同步的窄角和广角相机捕捉，覆盖城市和半城市道路。数据集包含10种不同的交通灯类别，旨在为自动驾驶车辆提供远距离交通灯检测的重要信息。创建过程中，数据收集在交通灯密度高的地区进行，使用CVAT工具手动标注。该数据集主要应用于自动驾驶技术，以提高交通灯检测的准确性和实时性。

DualCam is an innovative benchmark dataset for traffic light detection, developed by the Department of Electronics and Telecommunications Engineering of Murdoch University. It consists of 2250 annotated images captured by synchronized narrow-angle and wide-angle cameras, covering urban and semi-urban road scenarios. The dataset encompasses 10 distinct traffic light categories, and is designed to provide critical information for long-distance traffic light detection in autonomous vehicles. During its development, data was collected in areas with high traffic light density, and manual annotations were performed using the CVAT tool. This dataset is primarily applied in autonomous driving technologies to improve the accuracy and real-time performance of traffic light detection.