Urban Object Detection
2D Box
Autonomous Driving
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许可协议: Custom

Overview

We propose a new dataset that is used for benchmarking the accuracy of a real-time object detector (Faster R-CNN). Part of the data was collected using an HD camera mounted on a vehicle. Furthermore, some of the data is weakly annotated so it can be used for testing weakly supervised learning techniques. There already exist urban object datasets, but none of them include all the essential urban objects. We carried out extensive experiments demonstrating the effectiveness of the baseline approach. Additionally, we propose an R-CNN plus tracking technique to accelerate the process of real-time urban object detection.

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Citation

@article{dominguez2018new,
  title={A new dataset and performance evaluation of a region-based cnn for urban object detection},
  author={Dominguez-Sanchez, Alex and Cazorla, Miguel and Orts-Escolano, Sergio},
  journal={Electronics},
  volume={7},
  number={11},
  pages={301},
  year={2018},
  publisher={Multidisciplinary Digital Publishing Institute}
}

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Custom

数据概要
数据格式
Image,
数据量
106.918K
文件大小
22.44GB
发布方
RoViT
The Robotics and Tridimensional Vision Group (RoViT) researches many aspects of mobile robotics using 3D data as the main source of perception.
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