Attain

An inclusive pavement distress dataset containing 2,293 images of 19,761 distress-type instances taken with smartphone cameras mounted on a vehicle's front and rear windshields. This dataset comprises images of ten different pavement distress types, including longitudinal and transverse cracks (linear cracks), alligator cracks, block cracks, weathering, lane/shoulder drop-off, raveling, patching and utility cuts, manholes, faded markings, and potholes. The Attain dataset's images have been manually annotated for object detection and classification purposes and can be used to train machine learning and deep learning models to detect and classify pavement distresses.本数据集为一款覆盖全面的路面病害（pavement distress）数据集，包含由安装于车辆前、后挡风玻璃的智能手机摄像头采集的2293张图像，共计19761个病害实例。该数据集涵盖10类不同的路面病害，具体包括纵向裂缝与横向裂缝（线性裂缝）、龟裂、块状裂缝、路面风化、车道/路肩边缘落差、集料松散、路面修补与管线开槽、检查井、标线褪色以及坑槽。本Attain数据集的图像已针对目标检测与分类任务完成人工标注，可用于训练机器学习与深度学习模型以实现路面病害的检测与分类。

搜集汇总

背景与挑战

背景概述

Attain数据集是一个包含2,293张图像、覆盖十种铺面病害类型的综合性数据集，图像通过车载智能手机摄像头拍摄，并已人工标注用于目标检测和分类任务，适用于训练机器学习和深度学习模型以自动检测和分类铺面病害。