基于双分支点流语义先验的路面病害分割模型

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维普

中文摘要：针对基于深度学习的真实路面病害图像识别算法主要面临的复杂道路背景与病害前景比例不同、病害尺度小等导致的类别严重不平衡、路面病害与道路的几何结构特征对比不明显导致其不易识别等问题,本文提出一种基于双分支语义先验网络,用于指导自注意力骨干特征网络挖掘背景与病害前景的复杂关系,运用高效自注意力机制和互协方差自注意力机制分别对二维空间和特征通道进行语义特征提取,并引入语义局部增强模块提高局部特征聚合能力.本文提出了一种新的稀疏主体点流模块,并与传统特征金字塔网络相结合,进一步缓解路面病害的类别不平衡问题;构建了一个真实场景的道路病害分割数据集,并在该数据集和公开数据集上与多个基线模型进行对比实验,实验结果验证了本模型的有效性.

外文标题：Segmentation model of pavement diseases based on semantic priori of double-branched point flow

外文摘要：At present,the main problems faced by real road disease image recognition algorithms based on deep learn-ing include serious imbalance in categories caused by different proportions of complex road background and foreground of diseases,and small disease scales.What's more,the inconspicuous contrast between pavement diseases and the geo-metric structure characteristics of roads leads to their difficulty in recognition.To address the above issues,we propose a semantic prior two-branch network to guide Transformer's backbone feature network in mining the complex relation-ship between background and foreground of pavement disease.It uses high-efficiency self-attention mechanism and cross-covariance image transformers(XCiT)to extract semantic features from two-dimensional space and feature chan-nels,respectively,and a semantic locally-enhanced feed-forward(SLeff)module to improve the ability of local feature aggregation.We also propose a new sparse subject sampling point stream module,which is combined with the tradition-al FPN structure to further alleviate the category imbalance problem of pavement diseases.Finally,we constructed the road disease segmentation dataset based on real scene and compared it with multiple baseline models on this dataset and public dataset.The experimental results demonstrated effectiveness of this model.

外文关键词：

semantic priori informationefficient attention mechanismcross-covariance image transformers attention mechanismsparse subject sampling point flowcategory imbalancesemantic segmentationpavement diseasesdeep learning

作者：

庞荣、杨燕、冷雄进、张朋、刘言

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作者单位：

西南交通大学计算机与人工智能学院, 四川成都 611756

可持续城市交通智能化教育部工程研究中心,四川成都 611756

招商局重庆公路工程检测中心有限公司, 重庆 400067

国家山区公路工程技术研究中心,重庆 400067

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关键词：

语义先验信息高效注意力机制互协方差注意力机制稀疏主体点流类别不平衡语义分割路面病害深度学习

基金：

国家自然科学基金国家重大研发计划重庆市技术创新与应用发展专项重点项目重庆市交通科技自筹项目

项目编号：

619762472019YFB-1310400CSTB2022TIAD-KPX0100CQJT20-22ZC05

出版年：

2024

DOI：

10.11992/tis.202306037

智能系统学报

中国人工智能学会　哈尔滨工程大学

智能系统学报

CSTPCD北大核心

影响因子：0.672

ISSN：1673-4785

年,卷(期)：2024.19(1)

参考文献量35