首页|基于深度学习的市政道路路面病害检测模型设计与实现

基于深度学习的市政道路路面病害检测模型设计与实现

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为提高检测效果,文章基于深度学习技术,设计了一种市政道路路面病害检测模型.首先,收集典型市政道路路面病害相关的图像数据,并对其进行标注,以供后续训练深度学习模型;其次,采集实际的市政道路路面图像,并基于自适应中值滤波对图像实施去噪处理;最后,利用标注的病害特征训练深度卷积神经网络,再将提取到的特征输入其中,通过网络学习实现对路面病害的准确检测.
Design and implementation of municipal road surface disease detection model based on deep learning
In order to improve the detection effect,this study designed a municipal road surface disease detection model based on deep learning technology.Firstly,the image data related to typical municipal road surface diseases were collected and labeled for the subsequent training of deep learning models.Secondly,the actual municipal road surface image is collected and denoised based on adaptive median filter.Finally,the marked disease features are used to train the deep convolutional neural network,and then the extracted features are input to achieve the accurate detection of road diseases through network learning.

municipal roadpavement diseasefeature extractiondeep convolutional neural networkdisease detection

宋大龙

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苏交科集团股份有限公司甘肃分公司,甘肃 兰州 730030

市政道路 路面病害 特征提取 深度卷积神经网络 病害检测

2024

中国高新科技
中华预防医学会,国家食品安全风险评估中心

中国高新科技

ISSN:
年,卷(期):2024.(9)
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