基于颜色信息的自动掩码生成，精准识别水下异常反光区域
Color Information-Based Automated Mask Generation for Detecting Underwater Atypical Glare Areas
- 融合多色空间与空间坐标，无监督聚类定位反光区域
- 在真实水下图像中检测到呼吸气泡区域，准确率显著提升
- 适合水下机器人安全监测，无需标注数据
水下潜航辅助与安全支持机器人通过机载水下摄像头获取实时潜水员信息。本研究提出一种基于无监督K-means聚类的呼吸气泡检测算法，解决了深度学习模型对高精度数据的需求以及构建监督数据集的困难。该方法融合水下图像的颜色数据与相对空间坐标，采用CLAHE增强对比度以降低噪声，随后进行像素聚类，分离出反射区域。实验结果表明，该算法能有效检测水下图像中的呼吸气泡区域；结合RGB、LAB和HSV三种颜色空间可显著提高检测精度。本研究为监测潜水员状态及识别潜在设备故障提供了基础支持。
原文摘要 · Abstract (English)
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.
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