用AI自动识别孟加拉国城市中的电动三轮车，提升智能交通监管效率。
Detecting Unauthorized Vehicles using Deep Learning for Smart Cities: A Case Study on Bangladesh
- 基于YOLOv8实现实时车辆检测，区分电动与人力三轮车。
- 在1730张标注图像上达到mAP50 83.447%，精度与召回超78%。
- 数据集已公开，适合城市交通管理与计算机视觉研究者使用。
南亚地区交通工具受地理和文化影响差异显著，孟加拉国城市中三轮车是常见出行方式，可分为人力驱动的非机动车和机动的电动三轮车。由于交通规则限制电动三轮车进入特定路段，监控其动向十分必要，但现有监控系统因与非机动车外观相似而难以区分，人工分析又耗时。本文提出一种基于深度学习的自动检测方法，采用YOLOv8模型实现实时目标检测。为训练模型，构建了1,730张在不同交通条件下拍摄的标注图像。实验结果表明，该模型在密集与稀疏交通场景下均表现良好，mAP50达83.447%，二分类精度与召回率均高于78%。相关数据集已公开，可供后续研究使用。
原文摘要 · Abstract (English)
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.
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