Deep Learning-Enhanced Autonomous Submarine Imaging System for Underwater Bubble Detection

Sotiris Spanos, Christos Antoniou, Simon Vellas, Valsamis Ntouskos, Angelos Mallios, Paraskevi Nomikou, Konstantinos Karantzalos

Abstract: Underwater environments provide significant opportunities for innovative research in various scientific fields and can have high economic interest in numerous commercial applications. To monitor diverse phenomena in an unattended manner, it is necessary to develop suitable observatory systems. In this work we describe the development of an autonomous submarine imaging system for bubble detection, the implementation of which addresses the challenges of deployment in extreme environments, such as those encountered in highly active underwater hydrothermal fields. To address the bubble detection problem, we train state-of-the-art object detection models using a manually collected dataset containing a large number of images in a real environment. Our models exhibit promising results both in terms of evaluation metrics and qualitative assessment.