Projects per year
Abstract
To ensure reliable object detection in autonomous systems, the detector must be able to adapt to changes in appearance caused by environmental factors such as time of day, weather, and seasons. Continually adapting the detector to incorporate these changes is a promising solution, but it can be computationally costly. Our proposed approach is to selectively adapt the detector only when necessary, using new data that does not have the same distribution as the current training data. To this end, we investigate three popular metrics for domain gap evaluation and find that there is a correlation between the domain gap and detection accuracy. Therefore, we apply the domain gap as a criterion to decide when to adapt the detector. Our experiments show that our approach has the potential to improve the efficiency of the detector's operation in real-world scenarios, where environmental conditions change in a cyclical manner, without sacrificing the overall performance of the detector. Our code is publicly available https://github.com/dadung/DGE-CDA.
Original language | English |
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Article number | 103885 |
Number of pages | 10 |
Journal | Computer Vision and Image Understanding |
Volume | 238 |
DOIs | |
Publication status | Published - Jan 2024 |
Keywords
- Continual domain adaptation
- Domain gap
- Object detection
Projects
- 1 Active
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Collaborative Sensing and Learning: Collaborative Sensing and Learning for Maritime Situational Awareness
Wagner, M., Chin, T. J., Reid, I., Gupta, S. & Guettier, C.
Australian Research Council (ARC), Safran Electronics & Defense Australasia Pty Ltd, Monash University – Internal Faculty Contribution, University of Adelaide
18/05/23 → 30/06/25
Project: Research