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Early Versus Late RGB–Infrared Fusion for Counter-UAV Detection: A Comparative Study

Alberto De Zan, Denis Tavaris, Gian Luca Foresti, Ivan Scagnetto · 2026 · IEEE Access

DOI: 10.1109/access.2026.3735919 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

この論文では、視覚的(RGB)および赤外線(IR)フレーム合併を通じてUAV検出のための新しい戦略を実施し、厳しい環境および遅い気象条件で正確さと精度を向上させます。この研究では、2つの対立したアプローチが研究され、実施されています:最初は、視覚レベル(早期)合併で、2つの異なるスペクトラを同時に適用できる共通の結果を作成することを目的として、視覚レベル(RGB)および赤外線(IR)合併で実施しています。反対に、第2のアプローチは、決定レベル(遅い)合併と呼ばれる専用環境で幅広くテストされており、低および高明度のシーン、さまざまな背景(天

Machine translation (Meta M2M100); the English original follows.

Abstract

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. 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). These tests allowed for the assessment of actual detection capabilities and system reliability in real-world applications. Furthermore, the two fusion strategies were evaluated and compared over a set of aligned images in order to highlight the better approach of the two. The results indicate that early fusion achieves better raw performance, since it incorporates the thermal information from the beginning of the training process. However, late fusion presents an innovative and useful detection workflow that can be applied in cases of sensor or frame misalignment. Both positive and negative aspects have also been pointed out and discussed, in order to give a more comprehensive overview. Finally, the paper outlines implementation considerations and practical recommendations based on operational priorities. Overall, the findings contribute to the research in RGB-IR fusion modalities, with the goal of enhancing UAV detection capabilities.

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Topics: Fusion, Computer science, Sensor fusion, Artificial intelligence, Medicine, Radar tracker

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