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 language | English |
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Title of host publication | Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1849-1858 |
Number of pages | 10 |
ISBN (Electronic) | 9798350318920 |
DOIs | |
State | Published - 3 Jan 2024 |
Event | 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 - Waikoloa, United States Duration: 4 Jan 2024 → 8 Jan 2024 |
Publication series
Name | Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 |
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Conference
Conference | 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024 |
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Country/Territory | United States |
City | Waikoloa |
Period | 4/01/24 → 8/01/24 |
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