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

• URL: http://arxiv.org/abs/2411.15110v1
• Date: Fri, 22 Nov 2024 18:21:20 GMT
• Title: A Real-Time DETR Approach to Bangladesh Road Object Detection for Autonomous Vehicles
• Authors: Irfan Nafiz Shahan, Arban Hossain, Saadman Sakib, Al-Mubin Nabil,
• Abstract summary: Detection Transformers has become a state of the art solution to object detection. Real-time DETR models are shown to perform significantly better on inference times. Our results gave a mAP50 score of 0.41518 in the public 60% test set, and 0.28194 in the private 40% test set.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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