Early versus Late RGB-Infrared Fusion for Counter-UAV Detection: A Comparative Study
This paper presents a novel strategy for UAV detection through visible (RGB) and infrared (IR) fusion, in order to improve accuracy and precision in harsh environments and weather conditions.
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Key points
- In this research, two opposite approaches have been studied and implemented: the first concerns image-level (early) fusion, which merges two different spectra into a single image to which one detector is applied.
- On the other hand, the second approach, called decision-level (late) fusion, focuses on blending together the output bounding boxes of two different detection pipelines, aiming to create a common result that can be applied to both spectra simultaneously.
- Both strategies have been extensively tested in a dedicated environment, which included alternating low and high brightness scenes, different backgrounds (sky and ground), distances (5, 25 and 60 m), altitudes and zoom levels (1X, 2X and 4X).
- Overall, the findings contribute to the research in RGB-IR fusion modalities, with the goal of enhancing UAV detection capabilities.
Sources (1)
- [1]Early versus Late RGB-Infrared Fusion for Counter-UAV Detection: A Comparative StudyarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 02:50 PM
This paper presents a novel strategy for UAV detection through visible (RGB) and infrared (IR) fusion, in order to improve accuracy and precision in harsh environments and weather conditions.
In this research, two opposite approaches have been studied and implemented: the first concerns image-level (early) fusion, which merges two different spectra into a single image to which one detector is applied.
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