Journal Article

Artificial Reef Detection Method for Multibeam Sonar Imagery Based on Convolutional Neural Networks

Liu, Yanxiong, Dong, Zhipeng, Jiang, Fengbiao, Ding, Jisheng, Yang, Long, Feng, Yikai

2022

Overview

Artificial reef detection in multibeam sonar images is an important measure for the monitoring and assessment of biological resources in marine ranching. With respect to how to accurately detect artificial reefs in multibeam sonar images, this paper proposes an artificial reef detection framework for multibeam sonar images based on convolutional neural networks (CNN). First, a large-scale multibeam sonar image artificial reef detection dataset, FIO-AR, was established and made public to promote the development of artificial multibeam sonar image artificial reef detection. Then, an artificial reef detection framework based on CNN was designed to detect the various artificial reefs in multibeam sonar images. Using the FIO-AR dataset, the proposed method is compared with some state-of-the-art artificial reef detection methods. The experimental results show that the proposed method can achieve an 86.86% F1-score and a 76.74% intersection-over-union (IOU) and outperform some state-of-the-art artificial reef detection methods.

• Publisher: MDPI AG
• Subject: Artificial neural networks / artificial reef detection / artificial reef detection dataset / Artificial reefs / Biomonitoring / Construction / convolutional neural networks / data collection / Datasets / deep learning / Design / Fisheries / Marine biology / Marine resources / multibeam sonar images / Neural networks / Remote sensing / Semantics / Sensors / Sonar