铺面渗水识别测试数据集

为提高隧道路面行车安全性，降低渗水对路面结构的影响，铺面渗水识别测试数据集主要面向隧道沥青路面渗水检测技术研究，满足渗水深度学习识别模型训练需求建设，本数据集基于重庆G348国道关长山隧道、赚宝隧道、天池隧道等运营隧道实地调研，采用像素为1200万的智能手机作为采集设备采集图像，主要采集了渗水沥青路面和干燥沥青路面图像，数据量为5.39GB。

To improve driving safety on tunnel pavements and mitigate the impact of water seepage on pavement structures, the Pavement Seepage Recognition Test Dataset was developed for research on water seepage detection technologies for tunnel asphalt pavements, and to meet the training requirements of deep learning-based seepage recognition models. This dataset is constructed based on field investigations of operating tunnels including Guanchangshan Tunnel, Zhuanbao Tunnel, and Tianchi Tunnel along National Highway G348 in Chongqing. Images were collected using a 12-megapixel smartphone as the acquisition equipment, mainly capturing images of water-seepaged asphalt pavements and dry asphalt pavements, with a total data volume of 5.39 GB.

搜集汇总

背景与挑战

背景概述

该数据集是一个用于隧道沥青路面渗水检测技术研究的测试数据集，旨在通过深度学习模型识别渗水情况以提高行车安全性。数据来源于重庆G348国道多个运营隧道的实地调研，使用智能手机采集了渗水和干燥沥青路面图像，总数据量为5.39GB，包含1575个文件，适用于模型训练和验证。