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

TL;DR

This paper presents an unsupervised, color-based mask generation method for detecting underwater breath bubbles, improving accuracy and robustness for diver safety monitoring without requiring extensive labeled datasets.

Contribution

It introduces a novel unsupervised clustering approach that fuses multiple color spaces and spatial data for effective underwater glare region detection.

Findings

Effective detection of breath bubbles in underwater images.

Combining RGB, LAB, and HSV color spaces improves accuracy.

The method supports diver safety and equipment monitoring.

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.