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

Source: [Gaining Scale Invariance in UAV Bird's Eye View Object Detection by Adaptive Resizing](<https://www.sota2.com/research/paper/68fa6c461e209131cea3327a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:14:32.298Z

Authors: Martin Messmer, Benjamin Kiefer, Andreas Zell

Published: 2021-01-29T17:26:38.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2101.12694>)

[PDF](<https://arxiv.org/pdf/2101.12694.pdf>)