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Enhancing Agricultural Image Segmentation with an Agricultural Segment Anything Model Adapter

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成果类型:
期刊论文
作者:
Li, Yaqin;Wang, Dandan;Yuan, Cao;Li, Hao;Hu, Jing
通讯作者:
Hu, J
作者机构:
[Li, Yaqin; Hu, Jing; Yuan, Cao; Hu, J; Li, Hao; Wang, Dandan] Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430024, Peoples R China.
通讯机构:
[Hu, J ] W
Wuhan Polytech Univ, Sch Math & Comp Sci, Wuhan 430024, Peoples R China.
语种:
英文
关键词:
image segmentation;adapters;agricultural image segmentation;zero-shot segmentation
期刊:
Sensors
ISSN:
1424-3210
年:
2023
卷:
23
期:
18
页码:
7884-
基金类别:
Conceptualization, C.Y. and D.W.; methodology, Y.L.; software, D.W.; validation, D.W.; formal analysis, C.Y. and Y.L.; investigation, H.L.; resources, J.H.; data curation, J.H.; writing—original draft preparation, D.W.; writing—review and editing, J.H. and H.L.; supervision, C.Y. and H.L.; project administration, J.H.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding.
机构署名:
本校为第一且通讯机构
院系归属:
数学与计算机学院
摘要:
The Segment Anything Model (SAM) is a versatile image segmentation model that enables zero-shot segmentation of various objects in any image using prompts, including bounding boxes, points, texts, and more. However, studies have shown that the SAM performs poorly in agricultural tasks like crop disease segmentation and pest segmentation. To address this issue, the agricultural SAM adapter (ASA) is proposed, which incorporates agricultural domain expertise into the segmentation model through a simple but effective adapter technique. By leveraging the distinctive characteristics of agricultural ...

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