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Article Dans Une Revue Remote Sensing Année : 2022

Mutual Guidance Meets Supervised Contrastive Learning: Vehicle Detection in Remote Sensing Images

Résumé

Vehicle detection is an important but challenging problem in Earth observation due to the intricately small sizes and varied appearances of the objects of interest. In this paper, we use these issues to our advantage by considering them results of latent image augmentation. In particular, we propose using supervised contrastive loss in combination with a mutual guidance matching process to helps learn stronger object representations and tackles the misalignment of localization and classification in object detection. Extensive experiments are performed to understand the combination of the two strategies and show the benefits for vehicle detection on aerial and satellite images, achieving performance on par with state-of-the-art methods designed for small and very small object detection. As the proposed method is domain-agnostic, it might also be used for visual representation learning in generic computer vision problems.
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Dates et versions

hal-03934152 , version 1 (02-06-2023)

Identifiants

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Hoàng-Ân Lê, Heng Zhang, Minh-Tan Pham, Sébastien Lefèvre. Mutual Guidance Meets Supervised Contrastive Learning: Vehicle Detection in Remote Sensing Images. Remote Sensing, 2022, 14 (15), pp.3689. ⟨10.3390/rs14153689⟩. ⟨hal-03934152⟩
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