# Evaluating YOLO Architectures: Implications for Real-Time Vehicle Detection in Urban Environments of Bangladesh

arXiv: https://arxiv.org/abs/2509.05652 · HTML: https://mlsanity.com/paper/2509.05652/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 6 Sep 2025.

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

- [RSUD20K: A Dataset for Road Scene Understanding In Autonomous Driving](https://mlsanity.com/paper/2401.07322/) (2024)
- [Freezing of Gait Detection Using Gramian Angular Fields and Federated Learning from Wearable Sensors](https://mlsanity.com/paper/2411.11764/) (2024)
- [Comparison of Object Detection Algorithms for Street-level Objects](https://mlsanity.com/paper/2208.11315/) (2022)
- [Bangladeshi Native Vehicle Detection in Wild](https://mlsanity.com/paper/2405.12150/) (2024)
- [SegFormer Fine-Tuning with Dropout: Advancing Hair Artifact Removal in Skin Lesion Analysis](https://mlsanity.com/paper/2509.02156/) (2025)

## Built on by (most cited first, 1 of 1)

- [Object Detection for Autonomous Driving in Chinese Rural Scenes: An Experimental Study on Real-Synthetic Data Mixing and Model Evaluation](https://mlsanity.com/paper/2607.27058/) (2026)

## Abstract (arXiv)

Vehicle detection systems trained on Non-Bangladeshi datasets struggle to accurately identify local vehicle types in Bangladesh's unique road environments, creating critical gaps in autonomous driving technology for developing regions. This study evaluates six YOLO model variants on a custom dataset featuring 29 distinct vehicle classes, including region-specific vehicles such as ``Desi Nosimon'', ``Leguna'', ``Battery Rickshaw'', and ``CNG''. The dataset comprises high-resolution images (1920x1080) captured across various Bangladeshi roads using mobile phone cameras and manually annotated using LabelImg with YOLO format bounding boxes. Performance evaluation revealed YOLOv11x as the top performer, achieving 63.7% mAP@0.5, 43.8% mAP@0.5:0.95, 61.4% recall, and 61.6% F1-score, though requiring 45.8 milliseconds per image for inference. Medium variants (YOLOv8m, YOLOv11m) struck an optimal balance, delivering robust detection performance with mAP@0.5 values of 62.5% and 61.8% respectively, while maintaining moderate inference times around 14-15 milliseconds. The study identified significant detection challenges for rare vehicle classes, with Construction Vehicles and Desi Nosimons showing near-zero accuracy due to dataset imbalances and insufficient training samples. Confusion matrices revealed frequent misclassifications between visually similar vehicles, particularly Mini Trucks versus Mini Covered Vans. This research provides a foundation for developing robust object detection systems specifically adapted to Bangladesh traffic conditions, addressing critical needs in autonomous vehicle technology advancement for developing regions where conventional generic-trained models fail to perform adequately.