# Investigating the Challenges of Class Imbalance and Scale Variation in Object Detection in Aerial Images

Source: [Investigating the Challenges of Class Imbalance and Scale Variation in Object Detection in Aerial Images](<https://www.sota2.com/research/paper/68fa6bc61e209131cea303eb>)

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

Data updated: 2025-10-25T23:57:05.246Z

Authors: Ahmed Elhagry, Mohamed Saeed

Published: 2022-02-05T04:48:33.000Z

## Abstract

While object detection is a common problem in computer vision, it is even more challenging when dealing with aerial satellite images. The variety in object scales and orientations can make them difficult to identify. In addition, there can be large amounts of densely packed small objects such as cars. In this project, we propose a few changes to the Faster-RCNN architecture. First, we experiment with different backbones to extract better features. We also modify the data augmentations and generated anchor sizes for region proposals in order to better handle small objects. Finally, we investigate the effects of different loss functions. Our proposed design achieves an improvement of 4.7 mAP over the baseline which used a vanilla Faster R-CNN with a ResNet-101 FPN backbone.

## 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/2202.02489>)

[PDF](<https://arxiv.org/pdf/2202.02489.pdf>)