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Urban Object Detection
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update dataset overview and ba...
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Feb 10, 2022 7:40 AM
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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.

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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🎉Many thanks to Graviti Open Datasets for contributing the dataset
Basic Information
Application ScenariosAutonomous Driving
AnnotationsBox2D
TasksNot Available
LicenseCustom
Updated on2022-02-10 07:40:28
Metadata
Data TypeImage
Data Volume106,918
Annotation Amount106,918
File Size22.56GB
Copyright Owner
RoViT
Annotator
Unknown