Original article was published by Namburi Srinath on Artificial Intelligence on Medium
NITCAD — An object detection, classification and stereo vision dataset for autonomous navigation in Indian roads
The area of autonomous vehicles (AVs) is booming. One particular domain which helps in the research of AVs is labelled data because it helps the ML algorithms (in most cases CNNs) to understand the scenario and act according to it.
India has some unique scenarios when it comes to roads such as
- Auto rickshaws are ubiquitous (which are not common in foreign roads, thus not available in foreign datasets)
- Unstructured environment in many roads
- Lack of lanes/dividers
So, it is important to collect, label and opensource data. As a small contribution, we have created an annotated dataset and published it in a conference. Please refer to https://doi.org/10.1016/j.procs.2020.04.022 for full paper which includes the evaluation of dataset and comparison of various metrics. This blog helps as an overview of our work and access dataset (fill the Google Form attached at the bottom).
Note: This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Entire NITCAD dataset has been divided into 2 parts
- NITCAD object dataset : Collected by using Noise Play 2 Action camera in 720p at 30fps.
- NITCAD stereo vision dataset : Collected by using Intel Realsense Depth camera D435
There are about 11,000 images with 4800 images manually labelled using ‘LabelBox’
Please fill this Google form to get access to NITCAD dataset.
Github link: https://github.com/NamburiSrinath/NITCAD-dataset (This repository contains our entire major project in which NITCAD dataset is a part)
Note: NITCAD Dataset stands for National Institute Of Technology Calicut Autonomous Driving dataset
P.S: I would like to thank my project teammates Athul Zac Joseph, Ch. Lakshmi Priyanka, Malavika Nair M, S Umamaheswaran and our guide Dr. Praveen Sankaran who helped at various stages during the project.