Detecting Unauthorized Vehicles using Deep Learning for Smart Cities: A Case Study on Bangladesh

TL;DR

This paper introduces a real-time deep learning system using YOLOv8 to accurately detect auto-rickshaws in traffic images, aiding traffic monitoring in Bangladesh's smart city initiatives.

Contribution

It presents a novel dataset and a YOLOv8-based model for auto-rickshaw detection, improving traffic surveillance accuracy in South Asian urban environments.

Findings

Achieved 83.447% mAP50 in detection accuracy

Binary precision and recall above 78%

Effective in both dense and sparse traffic scenarios

Abstract

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.