Modes of transportation vary across countries depending on geographical location and cultural context. In South Asian countries rickshaws are among the most common means of local transport. Based on their mode of operation, rickshaws in cities across Bangladesh can be broadly classified into non-auto (pedal-powered) and auto-rickshaws (motorized). Monitoring the movement of auto-rickshaws is necessary as traffic rules often restrict auto-rickshaws from accessing certain routes. However, existing surveillance systems make it quite difficult to monitor them due to their similarity to other vehicles, especially non-auto rickshaws whereas manual video analysis is too time-consuming. This paper presents a machine learning-based approach to automatically detect auto-rickshaws in traffic images. In this system, we used real-time object detection using the YOLOv8 model. For training purposes, we prepared a set of 1,730 annotated images that were captured under various traffic conditions. The results show that our proposed model performs well in real-time auto-rickshaw detection and offers an mAP50 of 83.447% and binary precision and recall values above 78%, demonstrating its effectiveness in handling both dense and sparse traffic scenarios. The dataset has been publicly released for further research.

翻译：交通方式因国家的地理位置和文化背景而异。在南亚国家，人力车是最常见的本地交通工具之一。根据其运行模式，孟加拉国各城市的人力车可大致分为非机动（脚踏驱动）和机动人力车。监控机动人力车的行驶是必要的，因为交通规则通常限制机动人力车进入某些路线。然而，由于它们与其他车辆（尤其是非机动人力车）的相似性，现有监控系统难以对其进行有效监测，而人工视频分析又过于耗时。本文提出了一种基于机器学习的方法，用于自动检测交通图像中的机动人力车。在该系统中，我们采用了YOLOv8模型进行实时目标检测。为训练模型，我们准备了一套包含1,730张标注图像的数据集，这些图像采集自多种交通条件。结果表明，我们提出的模型在实时机动人力车检测中表现良好，mAP50达到83.447%，二分类精确率和召回率均超过78%，证明了其在处理密集和稀疏交通场景中的有效性。该数据集已公开发布以供进一步研究。