构建首个覆盖多样印度交通场景的大规模目标检测数据集
DriveIndia: An Object Detection Dataset for Diverse Indian Traffic Scenes
- 采集3400+公里道路影像，涵盖城市、乡村、高速等复杂路况
- 含6.7万张高分辨率图像，24类物体标注，最先进模型达78.7% mAP50
- 专为应对雨雾天气、混杂车流等真实驾驶挑战设计，适合自动驾驶研究
我们提出DriveIndia，一个专为捕捉印度交通环境复杂性与不可预测性而构建的大规模目标检测数据集。该数据集包含66,986张高分辨率图像，以YOLO格式标注了24个与交通相关的物体类别，涵盖多种复杂条件：不同天气（雾、雨）、光照变化、道路设施差异以及密集混杂的交通模式。数据采集历时120小时以上，覆盖3,400+公里，涵盖城市、乡村及高速公路路段。该数据集为真实世界自动驾驶挑战提供全面基准。我们使用先进的YOLO系列模型进行基线测试，表现最佳的模型在mAP50上达到78.7%。驱动本数据集的设计旨在支持在不确定道路条件下实现鲁棒且可泛化的目标检测研究，未来将通过TiHAN-IIT Hyderabad数据集仓库公开发布（https://tihan.iith.ac.in/TiAND.html， terrestrial datasets -> camera dataset）。
原文摘要 · Abstract (English)
We introduce DriveIndia, a large-scale object detection dataset purpose-built to capture the complexity and unpredictability of Indian traffic environments. The dataset contains 66,986 high-resolution images annotated in YOLO format across 24 traffic-relevant object categories, encompassing diverse conditions such as varied weather (fog, rain), illumination changes, heterogeneous road infrastructure, and dense, mixed traffic patterns and collected over 120+ hours and covering 3,400+ kilometers across urban, rural, and highway routes. DriveIndia offers a comprehensive benchmark for real-world autonomous driving challenges. We provide baseline results using state-of-the-art YOLO family models, with the top-performing variant achieving a mAP50 of 78.7%. Designed to support research in robust, generalizable object detection under uncertain road conditions, DriveIndia will be publicly available via the TiHAN-IIT Hyderabad dataset repository https://tihan.iith.ac.in/TiAND.html (Terrestrial Datasets -> Camera Dataset).
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。