Data Structure ?
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約200万台の注釈付き車両を含む大型車両検出データセット。
高速道路における自動運転車の物体検出方法の訓練と評価に使用します。
高速道路における自動運転車の物体検出方法の訓練と評価に使用します。
概要:
20万枚の画像
199万の注釈付き車両
5メガピクセルの解像度
晴天、雨天、夕暮れ、夜間
空いている高速道路、渋滞、交通渋滞
車両の2Dボックスまたは3D立方体
軸に沿ったバウンディングボックス（AABB）と台形を用いて、より正確な記述を行います。 単純なAABBは、右の図に示すように、隣の車線に侵入することがあります。
最新の車両検出
どの方法を選びますか？何に焦点を当てますか？ 高精度、低メモリ使用量、低計算複雑性、リアルタイム検出、組み込みシステムなど。
引用
@inproceedings{boxy2019,
title={Boxy Vehicle Detection in Large Images},
author={Behrendt, Karsten},
booktitle={Proceedings of the IEEE International Conference on Computer Vision Workshops},
year={2019}
} }https://unsupervised-llamas.com/static/boxy/non_commercial_license.pdf
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- 1. The data originates from internet data collection or provided by service providers, and this platform offers users the ability to view and browse datasets.
- 2. This platform serves only as a basic information display for datasets, including but not limited to image, text, video, and audio file types.
- 3. Basic dataset information comes from the original data source or the information provided by the data provider. If there are discrepancies in the dataset description, please refer to the original data source or service provider's address.
- 1. All datasets on this site are copyrighted by their original publishers or data providers.
- 1. If you need to repost data from this site, please retain the original data source URL and related copyright notices.
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