Reflection Removal and Facial Detection of Individuals in Vehicles

Abstract

The research, "Reflection Removal and Facial Detection of Individuals in Vehicles," utilizes Single Image Reflection Removal (SIRR) technology and Face Detection to remove reflections and reduce glare caused by automotive glass and film. This enables the capture of facial images of individuals inside vehicles. SIRR technology enhances image quality by removing reflections from the surfaces of glass that might obscure objects. In this research, we explore the use of three models specialized in SIRR and YOLOv7 for Face Detection. However, the pre-trained models for reflection removal failed to effectively remove reflections and reduce glare from films. In this paper, we propose an approach to enhance the efficiency of removing reflections and reducing glare caused by automotive glass and film with opacities set at 40% and 60%, achieving an impressive improvement in Peak Signal-to-Noise Ratio (PSNR) by approximately 42.96% and Structural Similarity Index (SSIM) by approximately 34.16% compared to the pre-trained models.