论文标题
增强大气湍流对热适应对象检测模型的影响
Augmentation of Atmospheric Turbulence Effects on Thermal Adapted Object Detection Models
论文作者
论文摘要
大气湍流对远程观察系统的图像质量有降解的影响。由于温度，风速，湿度等各种元素的结果，湍流的特征是大气的折射率随机波动。这是一种可能发生在各种成像光谱中的现象，例如可见光或红外带。在本文中，我们分析了大气湍流对热图像中对象检测性能的影响。我们使用几何湍流模型来模拟对中等尺度热图像集的湍流效应，即“ Flir ADAS V2”。我们将热域的适应性应用于最先进的对象探测器，并提出数据增强策略，以提高对象探测器的性能，该对象探测器利用不同严重性水平的湍流图像作为训练数据。我们的结果表明，提出的数据增强策略可以增加湍流和非扰动的热测试图像的性能。
Atmospheric turbulence has a degrading effect on the image quality of long-range observation systems. As a result of various elements such as temperature, wind velocity, humidity, etc., turbulence is characterized by random fluctuations in the refractive index of the atmosphere. It is a phenomenon that may occur in various imaging spectra such as the visible or the infrared bands. In this paper, we analyze the effects of atmospheric turbulence on object detection performance in thermal imagery. We use a geometric turbulence model to simulate turbulence effects on a medium-scale thermal image set, namely "FLIR ADAS v2". We apply thermal domain adaptation to state-of-the-art object detectors and propose a data augmentation strategy to increase the performance of object detectors which utilizes turbulent images in different severity levels as training data. Our results show that the proposed data augmentation strategy yields an increase in performance for both turbulent and non-turbulent thermal test images.
