Atmospheric turbulence poses a significant challenge to the performance of
object detection models. Turbulence causes distortions, blurring, and noise in
images by bending and scattering light rays due to variations in the refractive
index of air. This results in non-rigid geometric distortions and temporal
fluctuations in the electromagnetic radiation received by optical systems. This
paper explores the effectiveness of turbulence image augmentation techniques in
improving the accuracy and robustness of thermal-adapted and deep
learning-based object detection models under atmospheric turbulence. Three
distinct approximation-based turbulence simulators (geometric, Zernike-based,
and P2S) are employed to generate turbulent training and test datasets. The
performance of three state-of-the-art deep learning-based object detection
models: RTMDet-x, DINO-4scale, and YOLOv8-x, is employed on these turbulent
datasets with and without turbulence augmentation during training. The results
demonstrate that utilizing turbulence-specific augmentations during model
training can significantly improve detection accuracy and robustness against
distorted turbulent images. Turbulence augmentation enhances performance even
for a non-turbulent test set.

利用湍流图像增强技术改善在大气湍流环境下热适应和基于深度学习的目标检测模型的准确性和鲁棒性，研究表明，通过在模型训练中使用湍流特定的增强方法，可以显著提高检测湍流图像的准确性和鲁棒性。