用实时DETR检测孟加拉道路目标，兼顾速度与精度。
A Real-Time DETR Approach to Bangladesh Road Object Detection for Autonomous Vehicles
- 基于RTDETR架构，实现道路目标实时检测。
- 公开测试集mAP50达0.415，私有测试集0.282。
- 为自动驾驶在复杂发展中国家道路场景提供实用方案。
近年来，计算机视觉领域因变压器架构的出现发生范式转变。检测变压器已成为目标检测的前沿方案，是自动驾驶中道路目标检测的有力候选。尽管已有多种检测方法，实时DETR模型在推理时间上表现更优，且准确率和性能损失极小。本文在孟加拉国的BadODD道路目标检测数据集上应用了实时DETR（RTDETR）进行实验与测试。结果表明，在公开的60%测试集上mAP50达到0.41518，在私有的40%测试集上为0.28194。
原文摘要 · Abstract (English)
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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