Bangladeshi Native Vehicle Detection in Wild

Abstract

The success of autonomous navigation relies on robust and precise vehicle recognition, hindered by the scarcity of region-specific vehicle detection datasets, impeding the development of context-aware systems. To advance terrestrial object detection research, this paper proposes a native vehicle detection dataset for the most commonly appeared vehicle classes in Bangladesh. 17 distinct vehicle classes have been taken into account, with fully annotated 81542 instances of 17326 images. Each image width is set to at least 1280px. The dataset’s average vehicle bounding box-to-image ratio is 4.7036. This Bangladesh Native Vehicle Dataset (BNVD) has accounted for several geographical, illumination, variety of vehicle sizes, and orientations to be more robust on surprised scenarios. In the context of examining the BNVD dataset, this work provides a thorough assessment with four successive You Only Look Once (YOLO) models, namely YOLO v5, v6, v7, and v8. These dataset’s effectiveness is methodically evaluated and contrasted with other vehicle datasets already in use. The BNVD dataset exhibits mean average precision(mAP) at 50% intersection over union(IoU) is 0.848 corresponding precision and recall values of 0.841 and 0.774. The research findings indicate a mAP of 0.643 at an IoU range of 0.5 to 0.95. The experiments show that the BNVD dataset serves as a reliable representation of vehicle distribution and presents considerable complexities.

Dataset Overview

Total Images: 17,326
Instances: 81,542
BBox Per Image: 4.7036
Vehicle Categories:

1. Bicycle
2. Bus
3. Bhotbhoti
4. Car
5. CNG
6. Easybike
7. Leguna
8. Motorbike
9. MPV
10. Pedestrian
11. Pickup
12. PowerTiller
13. Rickshaw
14. ShoppingVan
15. Truck
16. Van
17. Wheelbarrow

Evaluation Results

Contributors

Citation

DOI: https://doi.org/10.48550/arXiv.2405.12150

        @misc{saha2024bangladeshi,
        title={Bangladeshi Native Vehicle Detection in Wild}, 
        author={Bipin Saha and Md. Johirul Islam and Shaikh Khaled Mostaque and 
        Aditya Bhowmik and Tapodhir Karmakar Taton and Md. Nakib Hayat Chowdhury and Mamun Bin Ibne Reaz},
        year={2024},
        eprint={2405.12150},
        archivePrefix={arXiv},
        primaryClass={cs.CV}}
                    

Dataset

Tools

• Notebooks
• Annotation Tools
• Dataset Tools