M.S. Candidate: Engin Uzun
Program: Multimedia Informatics
Date: 03.09.2024 / 13:30
Place: B-116

Abstract: Atmospheric turbulence, caused by factors such as temperature, wind speed, and humidity, leads to random fluctuations in the atmosphere's refractive index. This phenomenon degrades the image quality of long-range observation systems through geometric distortions and spatial-temporal varying blur. Turbulence can affect various imaging spectra, including visible and thermal bands. This thesis addresses the challenge of atmospheric turbulence in thermal imagery and its impact on object detection models. To tackle this challenge, we propose a data augmentation method that enhances the performance of object detectors by utilizing turbulent images with varying severity levels as training data. We generate training samples using a geometric turbulence simulator and use Geometric, Zernike-based, and P2S-based simulators to create the turbulent test sets, confirming the effectiveness of our augmentation method across different types of simulated turbulence. Our results demonstrate that this data augmentation approach significantly improves performance for both turbulent and non-turbulent thermal test images.