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

arXiv: https://arxiv.org/abs/2510.26154 · HTML: https://mlsanity.com/paper/2510.26154/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Road Damage and Manhole Detection using Deep Learning for Smart Cities: A Polygonal Annotation Approach](https://mlsanity.com/paper/2510.03797/) (2025)
- [Plant Disease Detection using Region-Based Convolutional Neural Network](https://mlsanity.com/paper/2303.09063/) (2023)
- [Finetuning YOLOv9 for Vehicle Detection: Deep Learning for Intelligent Transportation Systems in Dhaka, Bangladesh](https://mlsanity.com/paper/2410.08230/) (2024)

## Built on by (most cited first, 7 of 7)

- [Evolving CNN Architectures: From Custom Designs to Deep Residual Models for Diverse Image Classification and Detection Tasks](https://mlsanity.com/paper/2601.01099/) (2026)
- [Evaluation of Convolutional Neural Network For Image Classification with Agricultural and Urban Datasets](https://mlsanity.com/paper/2601.01393/) (2026)
- [Experimental Comparison of Light-Weight and Deep CNN Models Across Diverse Datasets](https://mlsanity.com/paper/2601.03463/) (2026)
- [Comparative Analysis of Custom CNN Architectures versus Pre-trained Models and Transfer Learning: A Study on Five Bangladesh Datasets](https://mlsanity.com/paper/2601.04352/) (2026)
- [Performance Analysis of Image Classification on Bangladeshi Datasets](https://mlsanity.com/paper/2601.04397/) (2026)
- [Training a Custom CNN on Five Heterogeneous Image Datasets](https://mlsanity.com/paper/2601.04727/) (2026)
- [Reliable Deep Learning for Small-Scale Classifications: Experiments on Real-World Image Datasets from Bangladesh](https://mlsanity.com/paper/2601.11911/) (2026)

## Abstract (arXiv)

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.