Nguyen Tran Xuan Bach, Rosser Kent, Chahl Javaan
School of Engineering, University of South Australia, Mawson Lakes 5095, Australia.
Aerospace Division, Defence Science and Technology Group, Edinburgh 5111, Australia.
J Imaging. 2022 Apr 16;8(4):116. doi: 10.3390/jimaging8040116.
It is necessary to establish the relative performance of established optical flow approaches in airborne scenarios with thermal cameras. This study investigated the performance of a dense optical flow algorithm on 14 bit radiometric images of the ground. While sparse techniques that rely on feature matching techniques perform very well with airborne thermal data in high-contrast thermal conditions, these techniques suffer in low-contrast scenes, where there are fewer detectable and distinct features in the image. On the other hand, some dense optical flow algorithms are highly amenable to parallel processing approaches compared to those that rely on tracking and feature detection. A Long-Wave Infrared (LWIR) micro-sensor and a PX4Flow optical sensor were mounted looking downwards on a drone. We compared the optical flow signals of a representative dense optical flow technique, the Image Interpolation Algorithm (I2A), to the Lucas-Kanade (LK) algorithm in OpenCV and the visible light optical flow results from the PX4Flow in both X and Y displacements. The I2A to LK was found to be generally comparable in performance and better in cold-soaked environments while suffering from the aperture problem in some scenes.
有必要在使用热成像相机的机载场景中确定现有光流方法的相对性能。本研究调查了一种密集光流算法在地面14位辐射图像上的性能。虽然依赖特征匹配技术的稀疏技术在高对比度热条件下的机载热数据中表现非常出色，但这些技术在低对比度场景中表现不佳，因为图像中可检测到的明显特征较少。另一方面，与那些依赖跟踪和特征检测的算法相比，一些密集光流算法非常适合并行处理方法。一个长波红外（LWIR）微传感器和一个PX4Flow光学传感器向下安装在无人机上。我们将一种具有代表性的密集光流技术——图像插值算法（I2A）的光流信号与OpenCV中的Lucas-Kanade（LK）算法以及PX4Flow在X和Y位移方面的可见光光流结果进行了比较。结果发现，I2A与LK在性能上总体相当，在冷浸环境中表现更好，但在某些场景中存在孔径问题。
