IndraEye: Infrared Electro-Optical UAV-based Perception Dataset for Robust Downstream Tasks
Manjunath D, Aniruddh Sikdar, Prajwal Gurunath, Sumanth Udupa, Suresh Sundaram
Indian Institute of Science, Bangalore
{manjunathd1, aniruddhss, vssuresh}@iisc.ac.in
Dataset: Google Drive Code: Github
🔥News:
We have hosted a challenge at International Conference on Emerging Technology in Autonomous Aerial Vehicles (ETAAV) related to this datasets at more details can be found at: ETAAV conference, IISc, Bengaluru
Deep neural networks (DNNs) have demonstrated superior performance when trained on well-illuminated environments, given that the images are captured through an Electro-Optical (EO) camera, which offers rich texture content. In critical applications such as aerial surveillance, maintaining consistent reliability of DNNs throughout all times of the day is paramount, including during low-light conditions where EO cameras often struggle to capture relevant details. Furthermore, UAV-based aerial object detection encounters significant scale variability stemming from varying altitudes and slant angles, introducing an additional layer of complexity. Existing approaches consider only illumination change/style variations as the domain shift, while in aerial surveillance, correlation shifts also acts as a hindrance to the performance of DNNs. In this paper we propose a multi-sensor (EO-IR) labelled object detection dataset consisting of 5276 images with 142991 instances covering multiple viewing angles and altitudes, 7 backgrounds and at different times of the day. This dataset serves as an effective resource for UAV-based object detection, facilitating the development of robust DNNs capable of operating round-the-clock. Dataset and source codes are available at https://bit.ly/indraeye
Citation:
If you aim to publish your work using this dataset, kindly consider citing the paper using:
@InProceedings{D_2025_CVPR,
author = {D, Manjunath and Sikdar, Aniruddh and Gurunath, Prajwal and Udupa, Sumanth and Sundaram, Suresh},
title = {SAGA: Semantic-Aware Gray color Augmentation for Visible-to-Thermal Domain Adaptation across Multi-View Drone and Ground-Based Vision Systems},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops},
month = {June},
year = {2025},
pages = {4578-4588}
}