Abstract:
"This document addresses the challenge of semantic segmentation in underwater imagery, which is critical for improving marine exploration and understanding. Semantic segmentation's ability to distinguish small objects underwater has a direct impact on a variety of industries, from marine biology research to the development of autonomous underwater vehicles, by improving the accuracy and reliability of object detection in complex underwater environments.
The research presented herein investigates innovative data augmentation techniques, specifically patch augmentation, to address the limitations of current segmentation methods. This approach is critical for improving the accuracy of object detection and thus contributing to the larger field of underwater vision technology.
Throughout this study, the author systematically selects technologies, frameworks, and programming languages, culminating in the creation of a prototype system. This prototype embodies the research's core functionalities, such as improved object detection capabilities, and serves as a foundation for further exploration and improvement in the domain.
By delving into the complexities of data selection, system design, and initial implementation, this document not only demonstrates the potential for significant advances in underwater imaging, but also lays the groundwork for addressing larger challenges in marine research and autonomous navigation technologies. The study's comprehensive analysis and experimental application aim to provide a solid foundation for future research, with the goal of raising the accuracy and efficiency standards in underwater semantic segmentation.
Keywords: USeg, Underwater Images, Semantic Segmentation, Data Augmentation, Patch Augmentation, Deep learning
Subject Descriptors:
• Computer Vision → Deep Learning → Semantic Segmentation
• Data Processing → Image Processing → Data Augmentation
• Marine Science → Underwater Imaging"
