WaterSAM: Adapting SAM for Underwater Object Segmentation
Object segmentation, a key type of image segmentation, focuses on detecting and delineating individual objects within an image, essential for applications like robotic vision and augmented reality. Despite advancements in deep learning improving object segmentation, underwater object segmentation re...
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MDPI AG
2024-09-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/12/9/1616 |
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| author | Yang Hong Xiaowei Zhou Ruzhuang Hua Qingxuan Lv Junyu Dong |
| author_facet | Yang Hong Xiaowei Zhou Ruzhuang Hua Qingxuan Lv Junyu Dong |
| author_sort | Yang Hong |
| collection | DOAJ |
| description | Object segmentation, a key type of image segmentation, focuses on detecting and delineating individual objects within an image, essential for applications like robotic vision and augmented reality. Despite advancements in deep learning improving object segmentation, underwater object segmentation remains challenging due to unique underwater complexities such as turbulence diffusion, light absorption, noise, low contrast, uneven illumination, and intricate backgrounds. The scarcity of underwater datasets further complicates these challenges. The Segment Anything Model (SAM) has shown potential in addressing these issues, but its adaptation for underwater environments, AquaSAM, requires fine-tuning all parameters, demanding more labeled data and high computational costs. In this paper, we propose WaterSAM, an adapted model for underwater object segmentation. Inspired by Low-Rank Adaptation (LoRA), WaterSAM incorporates trainable rank decomposition matrices into the Transformer’s layers, specifically enhancing the image encoder. This approach significantly reduces the number of trainable parameters to 6.7% of SAM’s parameters, lowering computational costs. We validated WaterSAM on three underwater image datasets: COD10K, SUIM, and UIIS. Results demonstrate that WaterSAM significantly outperforms pre-trained SAM in underwater segmentation tasks, contributing to advancements in marine biology, underwater archaeology, and environmental monitoring. |
| format | Article |
| id | doaj-art-2e49cab6eacd4c0181bf36801671ea05 |
| institution | OA Journals |
| issn | 2077-1312 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
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| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-2e49cab6eacd4c0181bf36801671ea052025-08-20T01:55:34ZengMDPI AGJournal of Marine Science and Engineering2077-13122024-09-01129161610.3390/jmse12091616WaterSAM: Adapting SAM for Underwater Object SegmentationYang Hong0Xiaowei Zhou1Ruzhuang Hua2Qingxuan Lv3Junyu Dong4School of Computer Science and Technology, West Coast Campus, Ocean University of China, No. 1299 Sansha Road, Binhai Street, Huangdao District, Qingdao 266100, ChinaSchool of Computer Science and Technology, West Coast Campus, Ocean University of China, No. 1299 Sansha Road, Binhai Street, Huangdao District, Qingdao 266100, ChinaSchool of Computer Science and Technology, West Coast Campus, Ocean University of China, No. 1299 Sansha Road, Binhai Street, Huangdao District, Qingdao 266100, ChinaSchool of Computer Science and Technology, West Coast Campus, Ocean University of China, No. 1299 Sansha Road, Binhai Street, Huangdao District, Qingdao 266100, ChinaSchool of Computer Science and Technology, West Coast Campus, Ocean University of China, No. 1299 Sansha Road, Binhai Street, Huangdao District, Qingdao 266100, ChinaObject segmentation, a key type of image segmentation, focuses on detecting and delineating individual objects within an image, essential for applications like robotic vision and augmented reality. Despite advancements in deep learning improving object segmentation, underwater object segmentation remains challenging due to unique underwater complexities such as turbulence diffusion, light absorption, noise, low contrast, uneven illumination, and intricate backgrounds. The scarcity of underwater datasets further complicates these challenges. The Segment Anything Model (SAM) has shown potential in addressing these issues, but its adaptation for underwater environments, AquaSAM, requires fine-tuning all parameters, demanding more labeled data and high computational costs. In this paper, we propose WaterSAM, an adapted model for underwater object segmentation. Inspired by Low-Rank Adaptation (LoRA), WaterSAM incorporates trainable rank decomposition matrices into the Transformer’s layers, specifically enhancing the image encoder. This approach significantly reduces the number of trainable parameters to 6.7% of SAM’s parameters, lowering computational costs. We validated WaterSAM on three underwater image datasets: COD10K, SUIM, and UIIS. Results demonstrate that WaterSAM significantly outperforms pre-trained SAM in underwater segmentation tasks, contributing to advancements in marine biology, underwater archaeology, and environmental monitoring.https://www.mdpi.com/2077-1312/12/9/1616underwater object segmentationunderwater imageSegment Anything Model (SAM) |
| spellingShingle | Yang Hong Xiaowei Zhou Ruzhuang Hua Qingxuan Lv Junyu Dong WaterSAM: Adapting SAM for Underwater Object Segmentation Journal of Marine Science and Engineering underwater object segmentation underwater image Segment Anything Model (SAM) |
| title | WaterSAM: Adapting SAM for Underwater Object Segmentation |
| title_full | WaterSAM: Adapting SAM for Underwater Object Segmentation |
| title_fullStr | WaterSAM: Adapting SAM for Underwater Object Segmentation |
| title_full_unstemmed | WaterSAM: Adapting SAM for Underwater Object Segmentation |
| title_short | WaterSAM: Adapting SAM for Underwater Object Segmentation |
| title_sort | watersam adapting sam for underwater object segmentation |
| topic | underwater object segmentation underwater image Segment Anything Model (SAM) |
| url | https://www.mdpi.com/2077-1312/12/9/1616 |
| work_keys_str_mv | AT yanghong watersamadaptingsamforunderwaterobjectsegmentation AT xiaoweizhou watersamadaptingsamforunderwaterobjectsegmentation AT ruzhuanghua watersamadaptingsamforunderwaterobjectsegmentation AT qingxuanlv watersamadaptingsamforunderwaterobjectsegmentation AT junyudong watersamadaptingsamforunderwaterobjectsegmentation |