DriveIndia: An Object Detection Dataset for Diverse Indian Traffic Scenes

TL;DR

DriveIndia is a large, diverse object detection dataset capturing Indian traffic scenes with challenging conditions, enabling improved autonomous driving research.

Contribution

It introduces a comprehensive, annotated dataset specifically designed for Indian traffic environments, covering diverse conditions and providing baseline detection results.

Findings

Top YOLO model achieved 78.7% mAP50 on DriveIndia.

Dataset includes 66,986 images across 24 object categories.

Captures complex, real-world Indian traffic scenarios.

Abstract

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).