Rain-Free Vision: Leveraging ICA for Enhanced Object Detection with YOLOv8 in Rainy Conditions

Autonomous vehicles in particular depends on visual data to detect and classify objects. However, in challenging weather conditions, such as rainy weather, the performance of object detection techniques may be hampered. In this paper, we introduced a meticulous approach to make the objects more visible with the help of advance pre-processing of the image. Our method uses Independent Component Analysis (ICA) to remove rain particles from images. ICA to separates two individual signals from a multivariate signal. We separate clear images from rainy images using ICA, involving a sequence of rotation, stretching, and re-rotation. Following the preprocessing stage, we implemented an object detection method to detect objects from refined image. For image deraining we achieved peak signal-to-noise ratio (PSNR) value of 33.39 and structural similarity index measure (SSIM) value of 0.985. We concluded up with discussing about some demerits of the method we used and the possible future research scope in this area.