# A Real-Time DETR Approach to Bangladesh Road Object Detection for Autonomous Vehicles

Source: [A Real-Time DETR Approach to Bangladesh Road Object Detection for Autonomous Vehicles](<https://www.sota2.com/research/paper/68fa75cf1e209131cea69f4b>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:57:11.277Z

Authors: Irfan Nafiz Shahan, Arban Hossain, Saadman Sakib, Al-Mubin Nabil

Published: 2024-11-22T18:21:20.000Z

## Abstract

In the recent years, we have witnessed a paradigm shift in the field of Computer Vision, with the forthcoming of the transformer architecture. Detection Transformers has become a state of the art solution to object detection and is a potential candidate for Road Object Detection in Autonomous Vehicles. Despite the abundance of object detection schemes, real-time DETR models are shown to perform significantly better on inference times, with minimal loss of accuracy and performance. In our work, we used Real-Time DETR (RTDETR) object detection on the BadODD Road Object Detection dataset based in Bangladesh, and performed necessary experimentation and testing. Our results gave a mAP50 score of 0.41518 in the public 60% test set, and 0.28194 in the private 40% test set.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2411.15110>)

[PDF](<https://arxiv.org/pdf/2411.15110.pdf>)