Faster R-CNN Based Deep Learning for Seagrass Detection from Underwater Digital Images

Md Moniruzzaman, Syed Mohammed Shamsul Islam, Paul Lavery, Mohammed Bennamoun

Research output: Chapter in Book/Conference paperConference paper

5 Citations (Scopus)

Abstract

Deep learning-based techniques have gained unprecedented success for object detection tasks. The state of the art object detection accuracy and robustness have been achieved by Faster R-CNN framework based algorithms. However, no attempts have been made to detect seagrasses from underwater images mostly due to lack of labelled ground truth dataset, and additional challenges imposed by underwater photographs and low boundary differences among the seagrass and surrounding vegetation. We have created a dataset consisting of 2,699 underwater images of Halophila ovalis (one of the common type of seagrasses from Indo-Pacific saltwater environments [1]). We have labelled the seagrass and implemented Faster R-CNN based object detector to detect them from underwater images. We have used Inception V2 network in the Faster R-CNN pipeline and found, this network showed a high mean average precision (mAP) of 0.3464 on laboratory images only, and 0.261 on a test set consists of both field and laboratory images.

Original languageEnglish
Title of host publication2019 Digital Image Computing
Subtitle of host publicationTechniques and Applications, DICTA 2019
PublisherIEEE, Institute of Electrical and Electronics Engineers
ISBN (Electronic)9781728138572
DOIs
Publication statusPublished - Dec 2019
Event2019 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2019 - Perth, Australia
Duration: 2 Dec 20194 Dec 2019

Publication series

Name2019 Digital Image Computing: Techniques and Applications, DICTA 2019

Conference

Conference2019 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2019
CountryAustralia
CityPerth
Period2/12/194/12/19

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