  We propose a comprehensive dataset for object detection in diverse driving
environments across 9 districts in Bangladesh. The dataset, collected
exclusively from smartphone cameras, provided a realistic representation of
real-world scenarios, including day and night conditions. Most existing
datasets lack suitable classes for autonomous navigation on Bangladeshi roads,
making it challenging for researchers to develop models that can handle the
intricacies of road scenarios. To address this issue, the authors proposed a
new set of classes based on characteristics rather than local vehicle names.
The dataset aims to encourage the development of models that can handle the
unique challenges of Bangladeshi road scenarios for the effective deployment of
autonomous vehicles. The dataset did not consist of any online images to
simulate real-world conditions faced by autonomous vehicles. The classification
of vehicles is challenging because of the diverse range of vehicles on
Bangladeshi roads, including those not found elsewhere in the world. The
proposed classification system is scalable and can accommodate future vehicles,
making it a valuable resource for researchers in the autonomous vehicle sector.
