Improved Target Detection With YOLOv8 for GAN Augmented Polarimetric Images Using MIRNet Denoising Model

Polarized images, which record the polarization characteristics of light, are becoming increasingly important in a variety of applications like remote sensing, medical imaging, and target detection. Their ability to offer additional information beyond traditional intensity-based images makes them valuable in situations where conventional imaging methods are lacking. However, the use of polarized images for tasks such as target detection presents challenges due to the limited availability of datasets, resulting in subpar performance in deep learning algorithms. Traditional methods for improving the quality of polarized images often involve noise reduction techniques, but these approaches may not fully exploit on the potential of deep learning algorithms due to limited dataset access. To get over this restriction and improve the performance of deep learning models on polarised images, new approaches are required. In this study, a new approach is proposed to address the challenges linked with polarized images by harnessing the capabilities of GAN and deep learning models. Specifically, the MIRNet CNN algorithm is utilized to denoise enhanced polarized datasets produced by GANs. By training the deep learning model on these enhanced datasets, the aim is to boost the performance of subsequent tasks like target detection. The study demonstrates the efficacy and efficiency of this novel approach for bettering the polarised image performance of deep learning models, particularly the MIRNet and YOLOv8 models. Through the use of GAN-generated enhanced datasets, there is a notable enhancement in the accuracy of target detection utilizing YOLOv8. This highlights the potential of this approach not only in target detection but also in various other fields that rely on precise object detection and image denoising utilizing polarized images.