Session: 05-01-01 New Concept and Marine Environment for OSU

Submission Number: 155118

Investigating the Ecological Impacts of Marine Debris on Fish Habitats Using High-Resolution Acoustic Cameras and AI-Driven Analysis 

The impact of artificial marine debris on fish habitat behavior remains unclear. For instance, whether fish migrate to areas with lower densities of marine debris or remain near debris piles is still an unresolved question.

It is noteworthy that marine debris piles typically do not form instantaneously but rather accumulate gradually from scattered objects over time. During this process, potential changes may occur in fish populations, species composition, behavioral patterns, habitat preferences, and interactions with the surrounding environment. However, these dynamic mechanisms require further investigation and elucidation.

In clear and shallow waters, fish behavior can be monitored through direct visual observation or optical cameras. However, most marine environments are characterized by low visibility, such as turbid or dark conditions, making it challenging to directly observe interactions between marine debris and fish. In recent years, the development of high-resolution acoustic cameras, also known as active forward-looking sonars, has provided an effective tool for studying fish behavior in low-visibility conditions. These sensors not only enable real-time monitoring of fish near marine debris but also generate quantitative data on fish size, activity depth, movement speed, and distance from the sensor. Furthermore, with millimeter-level resolution and a monitoring range spanning tens of meters, acoustic cameras are well-suited for investigating the relationships between marine debris, fish, and their habitats.

This study aims to utilize high-resolution acoustic cameras to acquire 3D spatial distribution data of marine debris and explore its potential impact on fish population size and species diversity. These efforts aim to provide objective data support and scientific evidence for the effective management of marine ecosystems. However, a significant challenge arises from the visual similarity of many artificial marine debris items, such as bottles and cans, to fish in sonar images. This ambiguity complicates the accurate estimation of debris abundance and fish population density, thereby affecting analyses of their interactions.

To address this issue, the study proposes an intelligent statistical approach based on sonar machine vision and deep learning. By deeply analyzing sonar image features, the method enables accurate classification and quantification of artificial marine debris and fish. Specific techniques include contrastive learning algorithms based on deep convolutional neural networks, sonar physical modeling, debris abundance estimation, and fish behavior analysis. This study employs an artificial intelligence (AI)--driven approach that relies minimally on prior ecological knowledge, thereby avoiding excessive constraints on environmental characteristics. Therefore, this pipeline enables a more objective analysis from diverse perspectives.

The ultimate outcome of this study is the development of an ecological index to quantify the impact of marine debris on fish ecosystems across different waters. This index will be integrated into marine ecological analysis models to evaluate the potential effects of marine debris on fish habitat selection, behavioral patterns, and community structures, as well as predict its long-term implications for ecosystems.

Presenting Author: Xiaoteng Zhou The University of Tokyo

Presenting Author Biography: He received the M.E. degree in Naval Architecture and Ocean Engineering from the Harbin Institute of Technology, Harbin, China, in 2022, where he is currently working toward the Ph.D. degree in The University of Tokyo, Tokyo, Japan. His research interests cover marine debris monitoring, underwater perception, machine vision, sonar signal and image processing and marine robotics.