# Towards Resolving the Challenge of Long-tail Distribution in UAV Images for Object Detection

arXiv: https://arxiv.org/abs/2011.03822 · HTML: https://mlsanity.com/paper/2011.03822/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2020.

## Built on by (most cited first, 20 of 20)

- [Deep Long-Tailed Learning: A Survey](https://mlsanity.com/paper/2110.04596/) (2021)
- [ACE: Ally Complementary Experts for Solving Long-Tailed Recognition in One-Shot](https://mlsanity.com/paper/2108.02385/) (2021)
- [A Survey on Long-Tailed Visual Recognition](https://mlsanity.com/paper/2205.13775/) (2022)
- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [Consistency-based Active Learning for Object Detection](https://mlsanity.com/paper/2103.10374/) (2021)
- [The State of Aerial Surveillance: A Survey](https://mlsanity.com/paper/2201.03080/) (2022)
- [Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement](https://mlsanity.com/paper/2103.10621/) (2021)
- [YOLC: You Only Look Clusters for Tiny Object Detection in Aerial Images](https://mlsanity.com/paper/2404.06180/) (2024)
- [UCDNet: Multi-UAV Collaborative 3D Object Detection Network by Reliable Feature Mapping](https://mlsanity.com/paper/2406.04648/) (2024)
- [E3-UAV: An Edge-based Energy-Efficient Object Detection System for Unmanned Aerial Vehicles](https://mlsanity.com/paper/2308.04774/) (2023)
- [Person Recognition in Aerial Surveillance: A Decade Survey](https://mlsanity.com/paper/2511.17674/) (2025)
- [Classification of Bark Beetle-Induced Forest Tree Mortality using Deep Learning](https://mlsanity.com/paper/2207.07241/) (2022)
- [AD-Det: Boosting Object Detection in UAV Images with Focused Small Objects and Balanced Tail Classes](https://mlsanity.com/paper/2504.05601/) (2025)
- [UAV-based Crowd Surveillance in Post COVID-19 Era](https://mlsanity.com/paper/2111.14176/) (2021)
- [ARUBA: An Architecture-Agnostic Balanced Loss for Aerial Object Detection](https://mlsanity.com/paper/2210.04574/) (2022)
- [Deep Learning-Based Object Detection in Maritime Unmanned Aerial Vehicle Imagery: Review and Experimental Comparisons](https://mlsanity.com/paper/2311.07955/) (2023)
- [SCLNet: A Scale-Robust Complementary Learning Network for Object Detection in UAV Images](https://mlsanity.com/paper/2409.07024/) (2024)
- [DREB-Net: Dual-stream Restoration Embedding Blur-feature Fusion Network for High-mobility UAV Object Detection](https://mlsanity.com/paper/2410.17822/) (2024)
- [Language-guided Learning for Object Detection Tackling Multiple Variations in Aerial Images](https://mlsanity.com/paper/2505.23193/) (2025)
- [Aligned Consensus Teaching for Label-Efficient Oriented Object Detection in Weakly-Aligned Visible-Infrared Imagery](https://mlsanity.com/paper/2609.18124/) (2026)

## Abstract (arXiv)

Existing methods for object detection in UAV images ignored an important challenge - imbalanced class distribution in UAV images - which leads to poor performance on tail classes. We systematically investigate existing solutions to long-tail problems and unveil that re-balancing methods that are effective on natural image datasets cannot be trivially applied to UAV datasets. To this end, we rethink long-tailed object detection in UAV images and propose the Dual Sampler and Head detection Network (DSHNet), which is the first work that aims to resolve long-tail distribution in UAV images. The key components in DSHNet include Class-Biased Samplers (CBS) and Bilateral Box Heads (BBH), which are developed to cope with tail classes and head classes in a dual-path manner. Without bells and whistles, DSHNet significantly boosts the performance of tail classes on different detection frameworks. Moreover, DSHNet significantly outperforms base detectors and generic approaches for long-tail problems on VisDrone and UAVDT datasets. It achieves new state-of-the-art performance when combining with image cropping methods. Code is available at https://github.com/we1pingyu/DSHNet