政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/154754

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 114105/145137 (79%)
Visitors : 52189041 Online Users : 892

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

政大機構典藏 > 資訊學院 > 資訊科學系 > 期刊論文 > Item 140.119/154754

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/154754

Title:	Blurriness-Guided Underwater Salient Object Detection and Data Augmentation
Authors:	彭彥璁 Peng, Yan-Tsung;Lin, Yu-Cheng;Peng, Wen-Yi;Liu, Chen-Yu
Contributors:	資訊系
Keywords:	Blurriness-guided;data augmentation (DA);underwater salient object detection (SOD)
Date:	2024-03
Issue Date:	2024-12-12 09:27:58 (UTC+8)
Abstract:	Salient object detection (SOD) has made significant progress with the help of deep networks. However, most works focus on terrestrial scenes, but underwater scenes for SOD are still little explored, which is essential for artificial-intelligence-driven underwater scene analysis. In the article, we propose and discuss two practical approaches to boost the performance of underwater SOD based on an inherent property of underwater scenes—blurriness, since an object appears more blurred when it is farther away. First, we utilize a self-derived blurriness cue and fuse it with the input image to help boost SOD accuracy. Next, we propose a blurriness-assisted data augmentation method that works for any available SOD model, called FocusAugment, for underwater SOD. We adjust images to enlarge differences between more- and less-focused regions based on the blurriness maps to augment training data. The experimental results show that both approaches can significantly improve state-of-the-art SOD models' accuracy for underwater scenes.
Relation:	IEEE Journal of Oceanic Engineering, Vol.49, No.3, pp.1089-1103
Data Type:	article
DOI 連結:	https://doi.org/10.1109/JOE.2023.3344154
DOI:	10.1109/JOE.2023.3344154
Appears in Collections:	[資訊科學系] 期刊論文

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	31	View/Open

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

Loading...