First qualitative observations on deep learning vision model YOLO and
  DETR for automated driving in Austria
Stefan Schoder
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.