| dc.contributor | 資訊系 | |
| dc.creator (作者) | 彭彥璁 | |
| dc.creator (作者) | Peng, Yan-Tsung;Lin, Yu-Cheng;Peng, Wen-Yi;Liu, Chen-Yu | |
| dc.date (日期) | 2024-03 | |
| dc.date.accessioned | 12-十二月-2024 09:27:58 (UTC+8) | - |
| dc.date.available | 12-十二月-2024 09:27:58 (UTC+8) | - |
| dc.date.issued (上傳時間) | 12-十二月-2024 09:27:58 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/154754 | - |
| dc.description.abstract (摘要) | 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. | |
| dc.format.extent | 104 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | IEEE Journal of Oceanic Engineering, Vol.49, No.3, pp.1089-1103 | |
| dc.subject (關鍵詞) | Blurriness-guided; data augmentation (DA); underwater salient object detection (SOD) | |
| dc.title (題名) | Blurriness-Guided Underwater Salient Object Detection and Data Augmentation | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1109/JOE.2023.3344154 | |
| dc.doi.uri (DOI) | https://doi.org/10.1109/JOE.2023.3344154 | |
