Seagrass detection using deep learning

Author Identifiers

ORCID: 0000-0003-1130-7078

Date of Award


Degree Type


Degree Name

Master of Science (Computer Science)


School of Science

First Advisor

Dr Syed Mohammed Shamsul Islam

Second Advisor

Associate Professor C. Peng Lam

Third Advisor

Professor Paul Lavery

Fourth Advisor

Mohammed Bennamoun

Field of Research Code

0801, 0502


Seagrasses play an essential role in the marine ecosystem by providing foods, nutrients, and habitat to the marine lives. They work as marine bioindicators by reflecting the health condition of aquatic environments. Seagrasses also act as a significant atmospheric carbon sink that mitigates global warming and rapid climate changes. Considering the importance, it is critical to monitor seagrasses across the coastlines which includes detection, mapping, percentage cover calculation, and health estimation. Remote sensing-based aerial and spectral images, acoustic images, underwater two-dimensional and three-dimensional digital images have so far been used to monitor seagrasses. For close monitoring, different machine learning classifiers such as the support vector machine (SVM), the maximum likelihood classifier (MLC), the logistic model tree (LMT) and the multilayer perceptron (MP) have been used for seagrass classification from two-dimensional digital images. All of these approaches used handcrafted feature extraction methods, which are semi-automatic.

In recent years, deep learning-based automatic object detection and image classification have achieved tremendous success, especially in the computer vision area. However, to the best of our knowledge, no attempts have been made for using deep learning for seagrass detection from underwater digital images. Possible reasons include unavailability of enough image data to train a deep neural network. In this work, we have proposed a Faster R-CNN architecture based deep learning detector that automatically detects Halophila ovalis (a common seagrass species) from underwater digital images.

To train the object detector, we have collected a total of 2,699 underwater images both from real-life shorelines, and from an experimental facility. The selected seagrass (Halophila ovalis) are labelled using LabelImg software, commonly used by the research community. An expert in seagrass reviewed the extracted labels. We have used VGG16, Resnet50, Inception V2, and NASNet in the Faster R-CNN object detection framework which were originally trained on COCO dataset. We have applied the transfer learning technique to re-train them using our collected dataset to be able to detect the seagrasses. Inception V2 based Faster R-CNN achieved the highest mean average precision (mAP) of 0.261.

The detection models proposed in this dissertation can be transfer learned with labelled two-dimensional digital images of other seagrass species and can be used to detect them from underwater seabed images automatically.

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