朱洪波,张在岩,秦育罗,宋伟东,张晋赫

• 1:辽宁工程技术大学测绘与地理科学学院
• 2:辽宁工程技术大学地理空间信息服务协同创新研究院
• 3:黑龙江科技大学矿业工程学院
• 4:宿迁学院建筑工程学院

摘要(Abstract)：

针对实际采集场景下路面影像中病害受背景纹理噪声影响程度大、病害边缘模糊导致分割不准确的问题，该文提出了一种基于Res＿UNet和全连接条件随机场的路面病害像素级精准检测方法：(1)对路面影像进行灰度化、中值滤波和自适应直方图均衡化等预处理；(2)根据辽宁省多年份实测路面影像制作大规模、多场景、像素级路面病害数据集，然后融合注意力机制及Dense Crf优化Res＿UNet网络结构完成模型训练；并引入损失函数dice loss增强了该方法对细小病害提取的能力；(3)将深度卷积神经网络分割后的路面病害特征图导入全连接条件随机场，对预测的路面病害结果进行轮廓优化，其检测结果为获取路面裂缝宽度，进而评估路面病害等级奠定了基础。该文选用2 000张辽宁省农村公路实测路面影像，并以人工判读作为标准，分别从准确率、召回率和精确率3个方面验证本文方法、分水岭算法和Res＿UNet模型在实际工作环境下的农村公路路面病害分割性能。结果表明，方法的准确率为91.3%,召回率为87.8%,精确率为87.5%,路面病害轮廓提取更加精细，能够适应于复杂路面条件下病害高鲁棒分割。

关键词(KeyWords)： 路面影像;病害分割;深度学习;全连接条件随机场;Res＿Unet

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(42071343);; 宿迁市指导性科技计划项目(Z2020138);; 2020年度黑龙江省省属高等学校基本科研业务费项目(2020-KYYWF-0690)

作者(Author): 朱洪波,张在岩,秦育罗,宋伟东,张晋赫

DOI: 10.16251/j.cnki.1009-2307.2022.09.021

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