报告详情
Attention-Guided Shape-Aware Double-branch Segmentation Network
编号：172
访问权限：仅限参会人
更新：2024-10-23 10:02:36 浏览：44次
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报告开始：暂无开始时间（Asia/Shanghai）
报告时间：暂无持续时间
所在会场：[暂无会议] [暂无会议段]
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摘要
In order to solve the problem of low segmentation accuracy caused by defect interference, dirty noise and lens blur under complex conditions, an attention-guided shape-aware double-branch segmentation network is proposed. Firstly, to solve the problem of low accuracy caused by deep to shallow semantic propagation errors, a semantic flow alignment module is proposed, which learns offsets between feature maps to assist information alignment. Secondly, an attention-guided self-selection fusion module is proposed, which combines the characteristics of deep information and shallow information to guide more accurate segmentation. At the same time, the shape-aware loss function is proposed to solve the problem of noise and target adhesion. This function uses shape features to guide the network to focus on the boundary region that is difficult to partition to improve segmentation performance. Comprehensive experiments on a self-built chip dataset confirm that this method improves feature representation and segmentation performance, with mIoU up to 94.4% (2.1%) and FPS up to 21%. In the CamVid dataset, mIoU is 65.1% (3.0% increase), and the number of parameters is reduced by 4.6%, which achieves a good balance between real-time and accuracy.
关键词
segmentation, double-branch network, semantic flow, attention mechanism, distance map
报告人
Zhuangzhishan
Research Assistant Jiangnan University WuJingjing
Associate professor Jiangnan University稿件作者
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重要日期
-
会议日期
10月31日
2024
至11月03日
2024
-
09月30日 2024
初稿截稿日期
-
11月12日 2024
注册截止日期
主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
联系方式
- Mrs. icsmd
- ic******@163.com
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