1. Development of Coral Investigation System Based on Semantic Segmentation of Single-Channel Images
- Author
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Zhang Yangfan, Syed Raza Mehdi, Hui Huang, Hong Song, Wang Wenxin, Kazim Raza, Wan Qixin, and Yichun Shentu
- Subjects
medicine.medical_specialty ,Channel (digital image) ,Computer science ,Coral ,Image processing ,spectral imaging ,02 engineering and technology ,010501 environmental sciences ,lcsh:Chemical technology ,01 natural sciences ,Biochemistry ,Convolutional neural network ,Article ,Analytical Chemistry ,convolutional neural networks ,Image Processing, Computer-Assisted ,0202 electrical engineering, electronic engineering, information engineering ,medicine ,Animals ,Segmentation ,lcsh:TP1-1185 ,Electrical and Electronic Engineering ,Instrumentation ,coral ,0105 earth and related environmental sciences ,Artificial neural network ,business.industry ,Deep learning ,deep learning ,Pattern recognition ,Anthozoa ,Magnetic Resonance Imaging ,Atomic and Molecular Physics, and Optics ,semantic segmentation ,Semantics ,Spectral imaging ,image processing ,Binary classification ,RGB color model ,020201 artificial intelligence & image processing ,Neural Networks, Computer ,Artificial intelligence ,business - Abstract
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.
- Published
- 2021