# Object Detection in Aerial Images: What Improves the Accuracy?

Source: [Object Detection in Aerial Images: What Improves the Accuracy?](<https://www.sota2.com/research/paper/68fa6ba61e209131cea2f923>)

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

Data updated: 2025-10-25T23:54:05.632Z

Authors: Hashmat Shadab Malik, Ikboljon Sobirov, Abdelrahman Mohamed

Published: 2022-01-21T16:22:48.000Z

## Abstract

Object detection is a challenging and popular computer vision problem. The problem is even more challenging in aerial images due to significant variation in scale and viewpoint in a diverse set of object categories. Recently, deep learning-based object detection approaches have been actively explored for the problem of object detection in aerial images. In this work, we investigate the impact of Faster R-CNN for aerial object detection and explore numerous strategies to improve its performance for aerial images. We conduct extensive experiments on the challenging iSAID dataset. The resulting adapted Faster R-CNN obtains a significant mAP gain of 4.96% over its vanilla baseline counterpart on the iSAID validation set, demonstrating the impact of different strategies investigated in this work.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2201.08763>)

[PDF](<https://arxiv.org/pdf/2201.08763.pdf>)