Active Learning for Single-Stage Object Detection in UAV Images

Asma Yamani*, Albandari Alyami, Hamzah Luqman, Bernard Ghanem, Silvio Giancola

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Unmanned aerial vehicles (UAVs) are widely used for image acquisition in various applications, and object detection is a crucial task for UAV imagery analysis. However, training accurate object detectors requires a large amount of annotated data, which can be expensive and time-consuming. To address this issue, we propose an active learning framework for single-stage object detectors in UAV images. First, we introduce Diverse Uncertainty Aggregation (DUA), a novel uncertainty aggregation method that aims to select images with a more diverse variety of object classes with high uncertainties. Second, we address the problem of class imbalance by adjusting the uncertainty calculation based on the performance of each class. Third, we illustrate how reducing the number of images for labeling does not necessarily lead to a lower labeling cost. Evaluation of our approach on a common UAV dataset shows that we can perform similarly (within 0.02 0.5mAP) to using the whole dataset while using only 25% of the images and 32% of the labeled objects. It also outperforms Random Selection and some other aggregation methods. Evaluation on VOC2012 show also consistent results utilizing only 25% of the labeling cost to reach a performance within 0.1 0.5mAP of using the whole dataset. Our results suggest that our proposed active learning framework can effectively reduce the annotation cost while improving the performance of singlestage object detectors in UAV image settings. The code is available on: https://github.com/asmayamani/DUA

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1849-1858
Number of pages10
ISBN (Electronic)9798350318920
DOIs
StatePublished - 3 Jan 2024
Event2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 - Waikoloa, United States
Duration: 4 Jan 20248 Jan 2024

Publication series

NameProceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024

Conference

Conference2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
Country/TerritoryUnited States
CityWaikoloa
Period4/01/248/01/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • Algorithms
  • Algorithms
  • Applications
  • Image recognition and understanding
  • Machine learning architectures
  • Remote Sensing
  • and algorithms
  • formulations

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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