Pavement defect data set under complex background

This study is based on the image data of cement concrete pavement diseases collected by myself. The mobile phone is fixed on the sun visor of the passenger seat of the vehicle, and all kinds of diseases on the road are photographed along with the vehicle. Based on 1,595 images, each image is expanded to 4 by using the data enhancement method. After screening, a total of 2,925 images are obtained, including 2,125 defective images with shadow occlusion and uneven illumination. In order to improve the robustness of the algorithm, 800 images are defect images collected in normal light, and 2925 photos are labeled by an opensource image LabelImg tool, and the defect images are divided into training set, verification set and test set with a ratio of 8: 1: 1, including 2340 training sets, 292 verification sets and 293 test sets.

本研究以自主采集的水泥混凝土路面病害图像数据为基础。将手机固定于车辆副驾驶遮阳板上，随车行进拍摄路面各类病害。原始数据集共包含1595张图像，通过数据增强方法将每张图像扩充为4张，经筛选后最终得到2925张图像，其中包括2125张存在阴影遮挡与光照不均问题的病害图像。为提升算法的鲁棒性，本次研究补充的800张病害图像均采集于光照正常的场景下。所有2925张图像均通过开源图像标注工具LabelImg完成标注，并将病害图像按8:1:1的比例划分为训练集、验证集与测试集，分别包含2340张、292张与293张图像。

搜集汇总

背景与挑战

背景概述

该数据集是一个专注于水泥混凝土路面病害检测的图像数据集，包含2,925张已标注的图像，其中2,125张具有阴影遮挡和光照不均的复杂背景，旨在提升算法在真实复杂环境下的鲁棒性。数据集已按8:1:1的比例划分为训练集、验证集和测试集，适用于计算机视觉和人工智能领域的模型训练与评估。