# Object Detection in Aerial Imagery

Source: [Object Detection in Aerial Imagery](<https://www.sota2.com/research/paper/68fa6e051e209131cea3d761>)

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

Data updated: 2025-10-26T00:48:10.793Z

Authors: Dmitry Demidov, Rushali Grandhe, Salem AlMarri

Published: 2022-11-15T11:22:18.000Z

## Abstract

Object detection in natural images has achieved remarkable results over the years. However, a similar progress has not yet been observed in aerial object detection due to several challenges, such as high resolution images, instances scale variation, class imbalance etc. We show the performance of two-stage, one-stage and attention based object detectors on the iSAID dataset. Furthermore, we describe some modifications and analysis performed for different models - a) In two stage detector: introduced weighted attention based FPN, class balanced sampler and density prediction head. b) In one stage detector: used weighted focal loss and introduced FPN. c) In attention based detector: compare single,multi-scale attention and demonstrate effect of different backbones. Finally, we show a comparative study highlighting the pros and cons of different models in aerial imagery setting.

## 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/2211.15479>)

[PDF](<https://arxiv.org/pdf/2211.15479.pdf>)