Development of Coral Investigation System Based on Semantic Segmentation of Single-Channel Images.

Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: MEDLINE

Original Publication: Basel, Switzerland : MDPI, c2000-

Anthozoa* ; Image Processing, Computer-Assisted*; Animals ; Magnetic Resonance Imaging ; Neural Networks, Computer ; Semantics

Among aquatic biota, corals provide shelter with sufficient nutrition to a wide variety of underwater life. However, a severe decline in the coral resources can be noted in the last decades due to global environmental changes causing marine pollution. Hence, it is of paramount importance to develop and deploy swift coral monitoring system to alleviate the destruction of corals. Performing semantic segmentation on underwater images is one of the most efficient methods for automatic investigation of corals. Firstly, to design a coral investigation system, RGB and spectral images of various types of corals in natural and artificial aquatic sites are collected. Based on single-channel images, a convolutional neural network (CNN) model, named DeeperLabC, is employed for the semantic segmentation of corals, which is a concise and modified deeperlab model with encoder-decoder architecture. Using ResNet34 as a skeleton network, the proposed model extracts coral features in the images and performs semantic segmentation. DeeperLabC achieved state-of-the-art coral segmentation with an overall mean intersection over union (IoU) value of 93.90%, and maximum F1-score of 97.10% which surpassed other existing benchmark neural networks for semantic segmentation. The class activation map (CAM) module also proved the excellent performance of the DeeperLabC model in binary classification among coral and non-coral bodies.

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31801619 National Science Foundation of China; 2016YFC1402403 National Key Research and Development Program of China; 2019C02050, and 2020C03012 Key Research and Development Plan of Zhejiang Province, China

Keywords: convolutional neural networks; coral; deep learning; image processing; semantic segmentation; spectral imaging

Date Created: 20210403 Date Completed: 20210427 Latest Revision: 20210427

20221213

PMC7961541

10.3390/s21051848

33800839