Deep learning for marine species recognition

Research output: Chapter in Book/Conference paperChapterpeer-review

17 Citations (Scopus)

Abstract

Research on marine species recognition is an important part of the actions for the protection of the ocean environment. It is also an under-exploited application area in the computer vision community. However, with the developments of deep learning, there has been an increasing interest about this topic. In this chapter, we present a comprehensive review of the computer vision techniques for marine species recognition, mainly from the perspectives of both classification and detection. In particular, we focus on capturing the evolution of various deep learning techniques in this area. We further compare the contemporary deep learning techniques with traditional machine learning techniques, and discuss the complementary issues between these two approaches. This chapter examines the attributes and challenges of a number of popular marine species datasets (which involve coral, kelp, plankton and fish) on recognition tasks. In the end, we highlight a few potential future application areas of deep learning in marine image analysis such as segmentation and enhancement of image quality.

Original languageEnglish
Title of host publicationHandbook of Deep Learning Applications
EditorsValentina Emilia Bales, Sanjiban Sekhar Roy, Dharmendra Sharma, Pijush Samui
Place of PublicationUSA
PublisherSpringer Science + Business Media
Pages129-145
Number of pages17
ISBN (Electronic)9783030114794
ISBN (Print)9783030114787
DOIs
Publication statusPublished - 1 Jan 2019

Publication series

NameSmart Innovation, Systems and Technologies
Volume136
ISSN (Print)2190-3018
ISSN (Electronic)2190-3026

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