Infrared and visible image fusion is a beneficial processing task for Unmanned Aerial Vehicle (UAV) surveillance, which can improve visibility by combining the advantages of the infrared camera and the visible light camera. An embedded onboard solution is necessary for UAV-based surveillance missions because it reduces the amount of data that are transmitted to the ground. In this paper, we propose an infrared and visible light image fusion method and implement it on two platforms with commonly used HW accelerators for embedded vision applications: Zedboard (ARM + FPGA) and NVIDIA TX1 (ARM + GPU), and compare their performances. To verify the usefulness of image fusion, we carry out sufficient experiments to prove that image fusion can improve the target detection ability of a UAV in different scenes. The detection rate for target detection is up to 0.926 in our experiments. The execution times on the ZedBoard and the TX1 are, respectively, 205.3 FPS and 36.6 FPS (38 \(\times\) and 6.7 \(\times\) in comparison to an ARM Cortex-A9 processor). Our results also show that the ZedBoard achieves an energy/frame reduction ratio of 7.1 \(\times\) and 18.9 \(\times\) respectively compared to the TX1 and the ARM CPU. This work is based on a UAV platform designed by ourselves, and all image sets are real scenes that we have captured. This demonstrates that the proposed method is viable and reflects the actual needs of real UAV surveillance systems.

红外和可见光图像融合是无人飞行器（UAV）监视的一项有益的处理任务，它可以通过结合红外摄像头和可见光摄像头的优势来提高可视性。对于基于UAV的监视任务，嵌入式机载解决方案是必需的，因为它可以减少传输到地面的数据量。在本文中，我们提出了一种红外和可见光图像融合方法，并将其在两个用于嵌入式视觉应用的常用硬件加速器平台上实现：Zedboard（ARM + FPGA）和NVIDIA TX1（ARM + GPU），并比较它们的性能。为了验证图像融合的有效性，我们进行了充分的实验，证明图像融合可以提高无人机在不同场景下的目标检测能力。在我们的实验中，目标检测的检测率高达0.926。ZedBoard和TX1的执行时间分别为205.3 FPS和36.6 FPS（38\（\ times \）和6.7 \（\ times \）（与ARM Cortex-A9处理器相比）。我们的结果还显示，与TX1和ARM CPU相比，ZedBoard的能量/帧减少比分别达到7.1 \（\ times \）和18.9 \（\ times \）。这项工作基于我们自己设计的无人机平台，所有图像集都是我们捕获的真实场景。这表明所提出的方法是可行的，并反映了真实的无人机监视系统的实际需求。