首页|基于语义分割的公路路面裂缝智能识别技术研究

基于语义分割的公路路面裂缝智能识别技术研究

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高速公路路面裂缝的实时检测和及时处理是保障车辆行车安全的关键环节与重要基础,特别是发生地质灾害导致路面开裂时,裂缝快速识别与对比是监测地质灾害发展变化的一种新手段.针对这类问题,该文提出一种基于语义分割的公路裂隙智能识别方法,通过数据集制作、神经网络搭建、计算参数以及评估指标4个部分搭建模型对公路裂缝进行快速识别.研究结果表明:① 该文搭建的神经网络AttentionU-net在语义分割公路裂隙时,二分类交叉损失函数值和准确率分别达到 0.008 7、0.998 4;② 语义分割法在公路裂隙智能识别中表现出更高精度,Dice相似系数为0.978,故与传统算法相比,语义分割法在公路裂隙智能识别方面具有更强的可靠性与优越性;③ 语义分割法对亮度和噪声有较好的鲁棒性和泛化能力.
Intelligent Recognition Technology of Highway Cracks Based on Semantic Segmentation
Real-time detection and timely treatment of highway cracks are crucially fundamental for vehicle safety.Rapid identification and comparison of cracks is a new method for monitoring the development and changes of geological disasters especially when they induce cracks.Therefore,this study proposed an intelligent recognition method of highway cracks based on semantic segmentation,establishing a model with dataset,neural network,calculation parameters,and evaluation indicators to rapidly identify the cracks.The results show that,firstly,when the neural network Attention U-net built in this study semantically segments highway cracks,the binary cross loss function value and accuracy rate reach 0.008 7 and 0.998 4,respectively.Secondly,compared with traditional algorithms,the semantic segmentation method shows higher accuracy,reliability,and superiority in intelligent recognition of highway cracks,with a Dice similarity coefficient of 0.978.Thirdly,the semantic segmentation method has better robustness and generalization ability to deal with brightness and noise.

highwaycracksemantic segmentationintelligent recognition

高亮、饶法强、杨忠民、代宗晟、孙浩

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广州大广高速公路有限公司,广东 广州 510900

中国铁道科学研究院集团有限公司 铁道建筑研究所,北京市 100081

北京科技大学 土木与资源工程学院,北京市 100083

高速公路 裂缝 语义分割 智能识别

广东交通集团重点研发项目

JT2023YB27

2024

中外公路
长沙理工大学

中外公路

CSTPCD
影响因子:0.626
ISSN:1671-2579
年,卷(期):2024.44(5)
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