An Indian Roads Dataset for Supported and Suspended Traffic Lights Detection

TL;DR

This paper introduces the Indian Roads Dataset (IRD), a comprehensive collection of over 8000 annotated images capturing Indian traffic lights in diverse conditions, aiming to enhance autonomous vehicle perception in developing nations.

Contribution

The paper presents a new, large-scale Indian traffic light dataset with extensive annotations, surpassing existing datasets in size, diversity, and real-world applicability.

Findings

IRD outperforms existing Indian traffic light datasets in size and variance.

The dataset includes images from multiple cities and lighting conditions.

Comparison shows IRD's potential to improve traffic light detection in Indian environments.

Abstract

Autonomous vehicles are growing rapidly, in well-developed nations like America, Europe, and China. Tech giants like Google, Tesla, Audi, BMW, and Mercedes are building highly efficient self-driving vehicles. However, the technology is still not mainstream for developing nations like India, Thailand, Africa, etc., In this paper, we present a thorough comparison of the existing datasets based on well-developed nations as well as Indian roads. We then developed a new dataset "Indian Roads Dataset" (IRD) having more than 8000 annotations extracted from 3000+ images shot using a 64 (megapixel) camera. All the annotations are manually labelled adhering to the strict rules of annotations. Real-time video sequences have been captured from two different cities in India namely New Delhi and Chandigarh during the day and night-light conditions. Our dataset exceeds previous Indian traffic light datasets in size, annotations, and variance. We prove the amelioration of our dataset by providing an extensive comparison with existing Indian datasets. Various dataset criteria like size, capturing device, a number of cities, and variations of traffic light orientations are considered. The dataset can be downloaded from here https://sites.google.com/view/ird-dataset/home