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.

翻译：我们提出了一个面向孟加拉国9个地区多样化驾驶环境的目标检测综合数据集。该数据集完全通过智能手机摄像头采集，真实再现了包含昼夜条件的现实场景。现有的大多数数据集缺乏适用于孟加拉国道路自动驾驶导航的合适类别，这使得研究人员难以开发能够处理复杂路况的模型。为解决这一问题，作者基于车辆特征而非当地名称提出了一套新的类别体系。该数据集旨在推动能够应对孟加拉国独特道路场景挑战的模型开发，以促进自动驾驶车辆的有效部署。数据集未包含任何在线图像，旨在模拟自动驾驶车辆面临的真实环境。由于孟加拉国道路上的车辆种类繁多（包括全球其他地区罕见的车型），车辆分类极具挑战性。所提出的分类体系具有可扩展性，能够兼容未来新车型，因此成为自动驾驶领域研究人员的宝贵资源。