A deep learning-based automated system for seabed imagery recognition and quantitative analysis

The demand for maritime space requires an integrated planning and management approach, which should be based on solid scientific knowledge and reliable mapping of the seabed. One of the widely used seabed mapping methods is underwater imagery. The main advantage of this method is its simplicity, enabling rapid collection of large amounts of data, and, hence, cost-effectiveness.

However, only a small part of information available in underwater imagery archives is being extracted due to labor-intensive and time-consuming analysis procedures. Efforts to develop automated techniques for UW image processing are challenged by the specifics of the UW environment, such as heterogeneity of substrate, variation in lighting, color instability, etc. Emerging novel deep learning (DL) methods open opportunities for more effective, accurate and rapid analysis of seabed images than ever before. Our project aims to develop an automated seabed imagery recognition and quantification method based on the DL approach.

The project consortium brings together specialists in signal, image and video processing, and marine benthic ecologists with long-term experience in UW research. We plan to develop a user-friendly system, flexible enough to use in a variety of marine environments. To test system’s capabilities, video material collected in the Arctic Ocean, Baltic Sea, Mediterranean Sea and other world regions will be used.

Project coordinator: Kaunas University of Technology

Project partners: Klaipėda University

Duration 2019–2022
Head of project
Antanas Verikas