eprintid: 17115
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/71/15
datestamp: 2023-12-19 03:23:34
lastmod: 2023-12-19 03:23:34
status_changed: 2023-12-19 03:07:29
type: article
metadata_visibility: show
creators_name: Hashmani, M.A.
creators_name: Umair, M.
title: A Novel Visual-Range Sea Image Dataset for Sea Horizon Line Detection in Changing Maritime Scenes
ispublished: pub
note: cited By 3
abstract: Sea horizon line (SHL) detection plays a pivotal role in the computational performance improvement of computer applications for the maritime environment by dividing the image into sea and sky regions. This division isolates the region of interest and reduces the computational cost of further processing. Testing and performance evaluation of SHL detection methods require a robust image dataset covering the maritime environment�s features at different geographical, seasonal, and maritime conditions. However, publicly available maritime image datasets are developed under a limited environment with slight-to-moderate variations in maritime features. This article proposes a novel sea image dataset that fills this gap by incorporating various geographical, seasonal, and maritime features. Across West Malaysia, one offshore and four geographically separated onshore locations were selected. On ten different occasions, field observations were recorded using a visual-range optical sensor and weather station. The data collection experiments were conducted between February 2020 until April 2021. The collected data were preprocessed and SHL images were selected based on their high feature diversity. Manual SHL annotation was applied on images, and a ground truth matrix was generated, which serves as a performance benchmark for SHL detection methods. As a result, the dataset presents 2673 high-definition (1920 � 1080 pixels) RGB images having a combination of 36 different geographical, seasonal, and maritime features to test and evaluate computer vision-based SHL detection methods. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
date: 2022
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124199539&doi=10.3390%2fjmse10020193&partnerID=40&md5=0ed1c658077deba626d27e52370f15e1
id_number: 10.3390/jmse10020193
full_text_status: none
publication: Journal of Marine Science and Engineering
volume: 10
number: 2
refereed: TRUE
issn: 20771312
citation:   Hashmani, M.A. and Umair, M.  (2022) A Novel Visual-Range Sea Image Dataset for Sea Horizon Line Detection in Changing Maritime Scenes.  Journal of Marine Science and Engineering, 10 (2).   ISSN 20771312