The DAMS-DETR model addresses challenges in infrared-visible object detection by proposing a Modality Competitive Query Selection strategy and a Multispectral Deformable Cross-attention module. The method outperforms state-of-the-art models on various datasets, demonstrating its effectiveness in handling complex scenes and misalignment issues.
The paper discusses the importance of infrared-visible object detection due to its ability to capture objects under challenging conditions like low illumination or smoke. It introduces the DAMS-DETR model based on DETR, aiming to fuse complementary information from infrared and visible images effectively.
Key components of DAMS-DETR include Modality Competitive Query Selection for dynamic feature selection and a Multispectral Deformable Cross-attention module for adaptive feature fusion. Experiments on different datasets show significant improvements over existing methods.
The study highlights the challenges of modality interference and misalignment in infrared-visible object detection, emphasizing the need for adaptive strategies like those proposed in DAMS-DETR. The model's performance is evaluated across multiple scenarios, showcasing its robustness and efficiency.
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by Guo Junjie,G... klokken arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00326.pdfDypere Spørsmål