基于深度学习的探地雷达在路面结构病害诊断中的应用  

Application of Deep Learning-based Ground Penetrating Radar in Pavement Structural Disease Diagnosis

作　　者：侯海 何坚 HOU Hai;HE Jian(Gezhouba Wuhan Road Materials Co.,Ltd.,Wuhan Hubei 430223,China)

机构地区：[1]葛洲坝武汉道路材料有限公司,湖北武汉430223

出　　处：《交通节能与环保》2025年第2期170-174,共5页Transport Energy Conservation & Environmental Protection

摘　　要：随着交通运输的迅速发展,路面结构的病害诊断和维护变得至关重要。三维探地雷达在路面结构病害诊断中取得了显著进展,已广泛应用于路面结构病害的诊断和评估。该技术通过非接触式的电磁波探测方法,能够实时获取路面病害的深度和位置信息。然而,由于路面病害种类繁多且复杂,传统的算法在目标识别和分类方面还存在局限性。深度学习是一种机器学习的方法,利用深层神经网络对数据进行训练和分类。通过使用深度学习技术,可以提高探地雷达对目标的识别准确性和效率,为路面结构病害诊断和维护提供更可靠的技术支持。With rapid development of transportation,the diagnosis and maintenance of pavement structural distresses have become increasingly critical.The application of three-dimensional ground-penetrating radar(GPR)in the diagnosis of pavement structural distresses has witnessed significant advancements and is now widely utilized in the diagnosis and assessment of pavement structural distresses.This technology utilizes the non-contact electromagnetic wave detection method to obtain real-time information on the depth and location of pavement distresses.However,traditional algorithms encounter limitations in target identification and classification due to the diverse and intricate nature of pavement distresses.Deep learning,a sophisticated machine learning method employing deep neural networks,plays a crucial role in training and classifying data.By incorporating deep learning techniques,the accuracy and efficiency of ground-penetrating radar targets recognition can be substantially improved.

关 键 词：探地雷达 路面结构病害诊断 目标识别 深度学习 

分 类 号：U416.2[交通运输工程—道路与铁道工程]