摘要
针对传统裂缝图像分割方法不能准确提取混凝土表面裂缝的难题,提出了一种改进的轻量级全局卷积网络的路面裂缝图像分割模型。根据深度卷积网络原理,使用大卷积核对裂缝图像进行分类和定位,针对裂缝特征构建轻量级的语义分割MobileNetv2-GCN模型。实验对比结果表明,该模型在三个公开裂缝数据集上都表现出优越的性能。采用中轴骨架算法提取语义分割后的裂缝骨架,计算裂缝平均宽度的物理值,其实验结果具有较高的准确性,可为公路健康检测提供可靠的数据支持。
To address the inability of traditional crack image segmentation methods to inaccurately extract the crack on the concrete surface,an improved lightweight global convolutional network crack image segmentation model is proposed in this study.Based on the principle of deep convolution network,the large convolution kernel is used to classify and locate crack images.For the characteristics of cracks,a lightweight semantic segmentation model MobileNetv2-GCN is constructed.Experimental results show that the MobileNetv2-GCN model delivers superior performance in three open crack datasets.The central axis skeleton algorithm is used to extract the crack skeleton subsequent to semantic segmentation,and the physical value of the average width of the crack is calculated.The proposed model has high accuracy and can provide reliable data support for road quality detection.
作者
李刚
高振阳
张新春
赵怀鑫
刘卓
Li Gang;Gao Zhenyang;Zhang Xinchun;Zhao Huaixin;Liu Zhuo(School of Electronic and Control Engineering,Chang′an University,Xi′an,Shaanxi 710064,China;Technology Quality Department,Shaanxi Province Railway Group Co.,Ltd.,Xi′an,Shaanxi 710199,China;Commission for Discipline Inspection and Supervision,Xi′an Xilan Natural Gas Group Co.,Ltd.,Xi′an,Shaanxi 710075,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2020年第8期103-111,共9页
Laser & Optoelectronics Progress
基金
国家自然科学基金(51408045)
西安市科学技术局科技创新引导项目[201805045YD23CG29(5)]
长安大学中央高校基本科研业务费专项资金(300102329203)。
关键词
图像处理
语义分割
大卷积核
全局卷积网络
平均交并比
骨架提取
image processing
semantic segmentation
large convolution kernel
global convolution network
mean intersection over union
skeleton extraction
