Title

Seagrass detection from underwater digital images using faster R-CNN with NASNet

Document Type

Conference Proceeding

Publication Title

2021 Digital Image Computing: Techniques and Applications (DICTA)

Publisher

IEEE

School

School of Science

RAS ID

40644

Comments

Noman, M. K., Islam, S. M. S., Abu-Khalaf, J., & Lavery, P. (2021, November-December). Seagrass detection from underwater digital images using faster R-CNN with NASNet [Paper presentation]. 2021 Digital Image Computing: Techniques and Applications (DICTA), Gold Coast, Australia.

https://doi.org/10.1109/DICTA52665.2021.9647325

Abstract

In recent years, it has been demonstrated that deep learning has great success in a variety of computer vision applications. Deep learning-based Faster R-CNN algorithm depends on region proposal network that provides state-of-the-art object detection performance. To date, a limited number of Faster R-CNN approaches have been attempted to detect seagrass from underwater digital images. This paper proposes an improved seagrass detector that enhances the detection performance by combining the Faster R-CNN framework with the NASNet-A backbone. This seagrass detector achieves a high mean average precision (mAP) of 0.412 on ECUHO-2 dataset, which is significantly better than state-of-the-art Halophila ovalis detection performance on this dataset.

DOI

10.1109/DICTA52665.2021.9647325

Access Rights

subscription content

Research Themes

Natural and Built Environments

Priority Areas

Environmental management, governance and policy

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