This study investigates the application of single and two-stage 2D-object
detection algorithms like You Only Look Once (YOLO), Real-Time DEtection
TRansformer (RT-DETR) algorithm for automated object detection to enhance road
safety for autonomous driving on Austrian roads. The YOLO algorithm is a
state-of-the-art real-time object detection system known for its efficiency and
accuracy. In the context of driving, its potential to rapidly identify and
track objects is crucial for advanced driver assistance systems (ADAS) and
autonomous vehicles. The research focuses on the unique challenges posed by the
road conditions and traffic scenarios in Austria. The country's diverse
landscape, varying weather conditions, and specific traffic regulations
necessitate a tailored approach for reliable object detection. The study
utilizes a selective dataset comprising images and videos captured on Austrian
roads, encompassing urban, rural, and alpine environments.

这项研究探讨了单阶段和两阶段的 2D 目标检测算法，例如 You Only Look Once (YOLO) 和 Real-Time DEtection TRansformer (RT-DETR) 算法在奥地利道路的自动化目标检测中的应用，以提高道路安全性。该研究利用奥地利道路上的图像和视频构建了选择性数据集，涵盖了城市、农村和高山环境，并针对奥地利道路的独特挑战进行了定制化的目标检测方法。