A Real-Time DETR Approach to Bangladesh Road Object Detection for Autonomous Vehicles
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866915031149445120 |
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| author | Shahan, Irfan Nafiz Hossain, Arban Sakib, Saadman Nabil, Al-Mubin |
| author_facet | Shahan, Irfan Nafiz Hossain, Arban Sakib, Saadman Nabil, Al-Mubin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15110 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Real-Time DETR Approach to Bangladesh Road Object Detection for Autonomous Vehicles Shahan, Irfan Nafiz Hossain, Arban Sakib, Saadman Nabil, Al-Mubin Computer Vision and Pattern Recognition 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. |
| title | A Real-Time DETR Approach to Bangladesh Road Object Detection for Autonomous Vehicles |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.15110 |