高速公路路面病态图像标注样本集

1、数据采集：通过多种设备采集的高速公路路面图像，对图像进行清晰度筛选，格式、尺寸的预处理，构成标准原始图像；
2、数据处理：1）预处理：对病害信息进行分类；2）病态定位：确定病害的特点，对病害信息进行分类；3）病态标注：采用标注软件labelImg对训练集中的图片上各种病态信息分别标注；标注的内容包括：病害位置、病害类型、病害参数；4）图像合并和压缩；
3、数据应用：标注后的图片与标准原始图像构成样本集，可用于卷积神经网络训练，构建识别模型；采用识别模型对原始图片进行智能分析后得到自动标注病害的路面图片。

1. Data Collection: Highway pavement images were collected via multiple devices, followed by clarity screening and preprocessing of image format and size to form standard raw images.
2. Data Processing: 1) Preprocessing: Classify pavement disease information; 2) Disease Localization: Determine the characteristics of pavement diseases and classify the pavement disease information; 3) Disease Annotation: Use the annotation software labelImg to separately annotate all types of pavement disease information on the images in the training set, with the annotation contents including disease location, disease type and disease parameters; 4) Image merging and compression;
3. Data Application: The annotated images and standard raw images constitute a sample set that can be used for convolutional neural network training to build recognition models. After performing intelligent analysis on raw images using the trained recognition models, pavement images with automatically annotated diseases can be obtained.

搜集汇总

背景与挑战

背景概述

该数据集是一个用于高速公路路面病态检测的图像标注样本集，由贵州宏信创达工程检测咨询有限公司自行采集和处理，数据规模为8M。它主要用于训练卷积神经网络，以构建智能识别模型，通过标注病害位置、类型和参数来辅助路面病害的自动分析和检测。