Color Information-Based Automated Mask Generation for Detecting Underwater Atypical Glare Areas

Abstract

Underwater diving assistance and safety support robots acquire real-time diver information through onboard underwater cameras. This study introduces a breath bubble detection algorithm that utilizes unsupervised K-means clustering, thereby addressing the high accuracy demands of deep learning models as well as the challenges associated with constructing supervised datasets. The proposed method fuses color data and relative spatial coordinates from underwater images, employs CLAHE to mitigate noise, and subsequently performs pixel clustering to isolate reflective regions. Experimental results demonstrate that the algorithm can effectively detect regions corresponding to breath bubbles in underwater images, and that the combined use of RGB, LAB, and HSV color spaces significantly enhances detection accuracy. Overall, this research establishes a foundation for monitoring diver conditions and identifying potential equipment malfunctions in underwater environments.