# IndraEye: Infrared Electro-Optical UAV-based Perception Dataset for Robust Downstream Tasks

arXiv: https://arxiv.org/abs/2410.20953 · HTML: https://mlsanity.com/paper/2410.20953/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 41 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 27 of 27)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection](https://mlsanity.com/paper/2203.16220/) (2022)
- [Oriented R-CNN for Object Detection](https://mlsanity.com/paper/2108.05699/) (2021)
- [CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion](https://mlsanity.com/paper/2211.14461/) (2022)
- [CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2303.11797/) (2023)
- [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018)
- [ReDet: A Rotation-equivariant Detector for Aerial Object Detection](https://mlsanity.com/paper/2103.07733/) (2021)
- [Drone-based Object Counting by Spatially Regularized Regional Proposal Network](https://mlsanity.com/paper/1707.05972/) (2017)
- [Cross-Domain Adaptive Teacher for Object Detection](https://mlsanity.com/paper/2111.13216/) (2021)
- [AU-AIR: A Multi-modal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance](https://mlsanity.com/paper/2001.11737/) (2020)
- [Contrastive Mean Teacher for Domain Adaptive Object Detectors](https://mlsanity.com/paper/2305.03034/) (2023)
- [SAMRS: Scaling-up Remote Sensing Segmentation Dataset with Segment Anything Model](https://mlsanity.com/paper/2305.02034/) (2023)
- [HIT-UAV: A high-altitude infrared thermal dataset for Unmanned Aerial Vehicle-based object detection](https://mlsanity.com/paper/2204.03245/) (2022)
- [SpaceNet MVOI: a Multi-View Overhead Imagery Dataset](https://mlsanity.com/paper/1903.12239/) (2019)
- [DAMSDet: Dynamic Adaptive Multispectral Detection Transformer with Competitive Query Selection and Adaptive Feature Fusion](https://mlsanity.com/paper/2403.00326/) (2024)
- [Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention Network](https://mlsanity.com/paper/2210.04367/) (2022)
- [RGB-D And Thermal Sensor Fusion: A Systematic Literature Review](https://mlsanity.com/paper/2305.11427/) (2023)
- [InfraParis: A multi-modal and multi-task autonomous driving dataset](https://mlsanity.com/paper/2309.15751/) (2023)
- [Self-training Guided Adversarial Domain Adaptation For Thermal Imagery](https://mlsanity.com/paper/2106.07165/) (2021)
- [D3T: Distinctive Dual-Domain Teacher Zigzagging Across RGB-Thermal Gap for Domain-Adaptive Object Detection](https://mlsanity.com/paper/2403.09359/) (2024)
- [NeighborTrack: Improving Single Object Tracking by Bipartite Matching with Neighbor Tracklets](https://mlsanity.com/paper/2211.06663/) (2022)

## Built on by (most cited first, 1 of 1)

- [Forging Spatial Intelligence: A Roadmap of Multi-Modal Data Pre-Training for Autonomous Systems](https://mlsanity.com/paper/2512.24385/) (2025)

## Abstract (arXiv)

Deep neural networks (DNNs) have shown exceptional performance when trained on well-illuminated images captured by Electro-Optical (EO) cameras, which provide rich texture details. However, in critical applications like aerial perception, it is essential for DNNs to maintain consistent reliability across all conditions, including low-light scenarios where EO cameras often struggle to capture sufficient detail. Additionally, UAV-based aerial object detection faces significant challenges due to scale variability from varying altitudes and slant angles, adding another layer of complexity. Existing methods typically address only illumination changes or style variations as domain shifts, but in aerial perception, correlation shifts also impact DNN performance. In this paper, we introduce the IndraEye dataset, a multi-sensor (EO-IR) dataset designed for various tasks. It includes 5,612 images with 145,666 instances, encompassing multiple viewing angles, altitudes, seven backgrounds, and different times of the day across the Indian subcontinent. The dataset opens up several research opportunities, such as multimodal learning, domain adaptation for object detection and segmentation, and exploration of sensor-specific strengths and weaknesses. IndraEye aims to advance the field by supporting the development of more robust and accurate aerial perception systems, particularly in challenging conditions. IndraEye dataset is benchmarked with object detection and semantic segmentation tasks. Dataset and source codes are available at https://bit.ly/indraeye.