Robust camera-independent color chart localization using YOLO

Accurate color information plays a critical role in numerous computer vision tasks, with the Macbeth ColorChecker being a widely used reference target due to its colorimetrically characterized color patches. However, automating the precise extraction of color information in complex scenes remains a challenge. In this paper, we propose a novel method for the automatic detection and accurate extraction of color information from Macbeth ColorCheckers in challenging environments. Our approach involves two distinct phases: (i) a chart localization step using a deep learning model to identify the presence of the ColorChecker, and (ii) a consensus-based pose estimation and color extraction phase that ensures precise localization and description of individual color patches. We rigorously evaluate our method using the widely adopted NUS and ColorChecker datasets. Comparative results against state-of-the-art methods show that our method outperforms the best solution in the state of the art achieving about 5% improvement on the ColorChecker dataset and about 17% on the NUS dataset. Furthermore, the design of our approach enables it to handle the presence of multiple ColorCheckers in complex scenes. Code will be made available after pubblication at: https://github.com/LucaCogo/ColorChartLocalization . • A robust and precise method for Camera-Independent Color Chart Localization is proposed. • The method has two phases: a chart localization step, and a consensus-based pose estimation. • The method can handle the presence of multiple color targets in complex scenes. • The method outperforms the state-of-the-art solution by up to about 17% on standard datasets.