# Tackling the Background Bias in Sparse Object Detection via Cropped Windows

Source: [Tackling the Background Bias in Sparse Object Detection via Cropped Windows](<https://www.sota2.com/research/paper/68fa6aa91e209131cea298b8>)

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

Data updated: 2025-10-25T23:30:26.199Z

Authors: Leon Amadeus Varga, Andreas Zell

Published: 2021-06-04T06:59:56.000Z

## Abstract

Object detection on Unmanned Aerial Vehicles (UAVs) is still a challenging task. The recordings are mostly sparse and contain only small objects. In this work, we propose a simple tiling method that improves the detection capability in the remote sensing case without modifying the model itself. By reducing the background bias and enabling the usage of higher image resolutions during training, our method can improve the performance of models substantially. The procedure was validated on three different data sets and outperformed similar approaches in performance and speed.

## 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/2106.02288>)

[PDF](<https://arxiv.org/pdf/2106.02288.pdf>)