# Gaining Scale Invariance in UAV Bird's Eye View Object Detection by Adaptive Resizing

arXiv: https://arxiv.org/abs/2101.12694 · HTML: https://mlsanity.com/paper/2101.12694/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 29 Jan 2021.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [An Analysis of Scale Invariance in Object Detection - SNIP](https://mlsanity.com/paper/1711.08189/) (2017)
- [Clustered Object Detection in Aerial Images](https://mlsanity.com/paper/1904.08008/) (2019)
- [Vision Meets Drones: A Challenge](https://mlsanity.com/paper/1804.07437/) (2018)
- [Learning Spatial Fusion for Single-Shot Object Detection](https://mlsanity.com/paper/1911.09516/) (2019)
- [Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach](https://mlsanity.com/paper/1908.03856/) (2019)
- [SpotNet: Self-Attention Multi-Task Network for Object Detection](https://mlsanity.com/paper/2002.05540/) (2020)

## Built on by (most cited first, 8 of 8)

- [SeaDronesSee: A Maritime Benchmark for Detecting Humans in Open Water](https://mlsanity.com/paper/2105.01922/) (2021)
- [Leveraging Synthetic Data in Object Detection on Unmanned Aerial Vehicles](https://mlsanity.com/paper/2112.12252/) (2021)
- [The State of Aerial Surveillance: A Survey](https://mlsanity.com/paper/2201.03080/) (2022)
- [The MONET dataset: Multimodal drone thermal dataset recorded in rural scenarios](https://mlsanity.com/paper/2304.05417/) (2023)
- [Memory Maps for Video Object Detection and Tracking on UAVs](https://mlsanity.com/paper/2303.03508/) (2023)
- [Stable Yaw Estimation of Boats from the Viewpoint of UAVs and USVs](https://mlsanity.com/paper/2306.14056/) (2023)
- [Person Recognition in Aerial Surveillance: A Decade Survey](https://mlsanity.com/paper/2511.17674/) (2025)
- [Comprehensive Analysis of the Object Detection Pipeline on UAVs](https://mlsanity.com/paper/2203.00306/) (2022)

## Abstract (arXiv)

This work introduces a new preprocessing step for object detection applicable to UAV bird's eye view imagery, which we call Adaptive Resizing. By design, it helps alleviate the challenges coming with the vast variances in objects' scales, naturally inherent to UAV data sets. Furthermore, it improves inference speed by two to three times on average. We test this extensively on UAVDT, VisDrone, and on a new data set we captured ourselves and achieve consistent improvements while being considerably faster. Moreover, we show how to apply this method to generic UAV object detection tasks. Additionally, we successfully test our approach on a height transfer task where we train on some interval of altitudes and test on a different one. Furthermore, we introduce a small, fast detector meant for deployment to an embedded GPU. Code will be made publicly available on our website.