论文标题
无人机对象检测使用RGB/IR融合
Drone Object Detection Using RGB/IR Fusion
论文作者
论文摘要
近年来，使用空中无人机图像进行对象检测引起了很多关注。在大多数情况下，可见光图像足以检测对象，但热摄像机可以将对象检测的功能扩展到夜间或遮挡的物体。因此，用于对象检测的RGB和红外（IR）融合方法很有用且重要。将深度学习方法应用于RGB/IR对象检测的最大挑战之一是缺乏用于无人机IR图像的可用培训数据，尤其是在夜间。在本文中，我们制定了几种使用Airsim模拟引擎和Cyclegan创建合成IR图像的策略。此外，我们利用一个照明感知的融合框架将RGB和IR图像融合，以在地面上检测对象检测。我们对模拟和实际数据进行表征和测试我们的方法。我们的解决方案是在实际无人机上运行的NVIDIA Jetson Xavier上实现的，每个RGB/IR图像对需要约28毫秒的处理。
Object detection using aerial drone imagery has received a great deal of attention in recent years. While visible light images are adequate for detecting objects in most scenarios, thermal cameras can extend the capabilities of object detection to night-time or occluded objects. As such, RGB and Infrared (IR) fusion methods for object detection are useful and important. One of the biggest challenges in applying deep learning methods to RGB/IR object detection is the lack of available training data for drone IR imagery, especially at night. In this paper, we develop several strategies for creating synthetic IR images using the AIRSim simulation engine and CycleGAN. Furthermore, we utilize an illumination-aware fusion framework to fuse RGB and IR images for object detection on the ground. We characterize and test our methods for both simulated and actual data. Our solution is implemented on an NVIDIA Jetson Xavier running on an actual drone, requiring about 28 milliseconds of processing per RGB/IR image pair.
