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Advancing Maritime Safety: Early Detection of Ship Fires through Computer Vision, Deep Learning Appr...

Advancing Maritime Safety: Early Detection of Ship Fires through Computer Vision, Deep Learning Appr...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_dbb09315e3b94f1a8d71517f5aa456d9

Advancing Maritime Safety: Early Detection of Ship Fires through Computer Vision, Deep Learning Approaches, and Histogram Equalization Techniques

About this item

Full title

Advancing Maritime Safety: Early Detection of Ship Fires through Computer Vision, Deep Learning Approaches, and Histogram Equalization Techniques

Publisher

Basel: MDPI AG

Journal title

Fire (Basel, Switzerland), 2024-03, Vol.7 (3), p.84

Language

English

Formats

Publication information

Publisher

Basel: MDPI AG

More information

Scope and Contents

Contents

The maritime sector confronts an escalating challenge with the emergence of onboard fires aboard in ships, evidenced by a pronounced uptick in incidents in recent years. The ramifications of such fires transcend immediate safety apprehensions, precipitating repercussions that resonate on a global scale. This study underscores the paramount importan...

Alternative Titles

Full title

Advancing Maritime Safety: Early Detection of Ship Fires through Computer Vision, Deep Learning Approaches, and Histogram Equalization Techniques

Authors, Artists and Contributors

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_dbb09315e3b94f1a8d71517f5aa456d9

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_dbb09315e3b94f1a8d71517f5aa456d9

Other Identifiers

ISSN

2571-6255

E-ISSN

2571-6255

DOI

10.3390/fire7030084

How to access this item