  Road scene understanding is crucial in autonomous driving, enabling machines
to perceive the visual environment. However, recent object detectors tailored
for learning on datasets collected from certain geographical locations struggle
to generalize across different locations. In this paper, we present RSUD20K, a
new dataset for road scene understanding, comprised of over 20K high-resolution
images from the driving perspective on Bangladesh roads, and includes 130K
bounding box annotations for 13 objects. This challenging dataset encompasses
diverse road scenes, narrow streets and highways, featuring objects from
different viewpoints and scenes from crowded environments with densely
cluttered objects and various weather conditions. Our work significantly
improves upon previous efforts, providing detailed annotations and increased
object complexity. We thoroughly examine the dataset, benchmarking various
state-of-the-art object detectors and exploring large vision models as image
annotators.
