2021/01/13 by Osama Mazhar, Mazhar, Osama, Jens Kober +1
Computer Science · Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Enhancement Techniques #Impact of Light on Environment and Health #Video Surveillance and Tracking Methods #cs.CV
paper · pdf · doi:10.48550/arxiv.2101.05361
openalex publication_date 2021/01/13 · arxiv created 2021/01/18 · openalex created_date 2021/01/18 · arxiv updated 2021/01/19 · openalex updated_date 2026/07/28
In this paper, we propose a new data augmentation method, Random Shadows and Highlights (RSH) to acquire robustness against lighting perturbations. Our method creates random shadows and highlights on images, thus challenging the neural network during the learning process such that it acquires immunity against such input corruptions in real world applications. It is a parameter-learning free method which can be integrated into most vision related learning applications effortlessly. With extensive experimentation, we demonstrate that RSH not only increases the robustness of the models against lighting perturbations, but also reduces over-fitting significantly. Thus RSH should be considered essential for all vision related learning systems. Code is available at: https://github.com/OsamaMazhar/Random-Shadows-Highlights.