[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"chains-groupcat":3,"experts-list":4,"experts-coverage":26,"tags-groups":28},[],{"list":5,"total":24,"page":24,"pageSize":25,"totalPages":24},[6],{"id":7,"name":8,"orgName":9,"school":10,"gender":11,"title":12,"degree":13,"honor":14,"position":14,"researchDirection":15,"intro":16,"province":17,"completeness":18,"avatarAuthorized":19,"sourceUrl":20,"nameValid":21,"tags":22,"tagNames":23},"7694407a-a422-3ab5-b16b-a0e05863f78a","左琛","长安大学运输工程学院","长安大学","男","讲师","工学博士",null,"1. 基于视觉基础模型的公路交通设施无人机巡检与智能图像识别方法研究伴随着飞控能力和计算平台的发展，无人机已经成为进行公路交通基础设施巡检的新兴工具。然而，无人机图像中扰动因素多、场景变化大，给后继的图像分析与智能识别方法带来巨大挑战。本课题组针对路面病害、标线标识、沿线设施、道路边坡等多种细分养护场景，以海量无人机航拍图像为数据基础，运用视觉基础模型、图像分割大模型、多模态数据处理等人工智能和计算机视觉前沿技术，探索各类异常结构的视觉响应特征，求解行业规范文本和巡检图像之间的相互对应关系，建立兼具计算效率和识别精度的智能图像分析方法，显著提高深度神经网络的训练速度、标注效率和泛化能力，为公路交通基础设施的状态检测和性能评估提供重要依据。1.1 基于基础模型的路面病害识别与搜索方法及时准确地发现裂缝、坑槽、修补等路面病害，是公路预防性养护和智慧化养护的重要内容。近年来，以卷积神经网络为核心的深度学习技术已经成为了分析巡检图像的主流方法。然而，当前技术多基于有监督学习框架，难以有效应对道路病害多样的形态特征，识别方法的泛化能力有限。本课题组基于无人机高分辨率图像，以视觉基础模型为核心技术手段，探索各类路面异常样例的高阶视觉特征，建立高性能路面病害识别方法。具体内容包括：(1)使用DINOv2、Web-SSL等视觉基础模型，充分利用自监督学习、Vision Transformer和海量无标注数据集，提取图像视觉特征；(2)运用距离度量、流形学习和无监督聚类技术，发现道路路面区域，移除不相关图像块；(3)使用XGBoost、TabPFN等有监督分类器，建立主动学习框架，促使计算机理解视觉特征与病害样本之间的对应关系，预测病害的发生位置、损坏类型和损坏程度；(4)基于典型病害样例库，运行近似近邻搜索方法，求解路面病害的类内多样性和场景变化性，实现异常结构的精细搜索与快速排查，探索道路病害的时空变化规律。1.2 图像分割大模型支持的交通标线缺损定位方法保障交通标线的可见性和清晰度，是道路设施养护的重要环节。基于深度神经网络的目标检测和语义分割方法是目前进行破损标线识别的主流技术手段。然而，神经网络的性能高度依赖人工标注的质量和规模，如何有效建立高质量数据集，提高神经网络的训练效率，减少人工标注工作量，成为了重要且紧迫的科研话题。本课题组引入图像分割大模型技术，依据标线颜色等先验信息，引导图像大模型理解交通设施养护场景，建立交通标线缺损快速检测和准确评估的技术方法。具体内容包括：(1)基于图像分割大模型SAM,启动全局分析模式，自动划分无人机巡检图像，依据颜色信息确定道路路面区域，划定研究范围；(2)使用DINOv2基础模型，运用Hausdorff距离和余弦距离，量化无人机图像序列的相似性，获得代表性图像样例；(3)基于代表性图像，训练YOLO和DETR目标检查网络，准确定位减速标线、车道线、导向箭头和导流线破损；(4)启动图像分割大模型SAM，分析交通标线样例，预测标线初始状态。基于阈值分割方法，提取标线的现存可见区域。计算标线的缺失区域比例，定量评估标线健康状态。1.3 多模态学习赋能的交通沿线设施快速识别方法交通标志、交安设施、交通监控等沿线设施是交通系统的基本组成，定位和维护沿线设施是进行基础设施养护的基本要素。基于YOLO等目标检测网络，计算机能够实现无人机航拍图像的高效处理。然而，当前的有监督学习方法高度依赖人工标注数据集，能以有效应对光照条件、巡检场景和飞控参数的显著变化，识别方法的泛化能力有限。本课题组引入多模态分析技术，探索交通标志图像和行业规范文本之间的映射关系，在共享特征空间内实现多模态样例的准确对齐和配准，提高交通沿线设施的快速定位和精细分类。具体内容如下：(1)启动Grounding DINO、YOLO-World、YOLOE等开放词汇目标检测程序，合理设定关键字提示词，建立沿线设施样例数据集；(2)运用知识蒸馏技术，建立教师-学生结构，去除任务无关神经元，获得小尺寸高效率目标检测网络；(3)使用视觉基础模型分析无人机图像，使用文本编码器分析行业规范文本，运用线性映射层实现共享特征空间内的样本对齐，实现交通沿线设施的精细化分类。1.4 光学、红外与三维点云联合驱动的道路边坡隐患识别方法快速发现边坡落石、岩体开裂、植被侵入等异常案例，是维护道路边坡安全的重要因素。在无人机智能巡检框架内，如何有效整合光学图像、红外图像、三维点云等多模态数据采集手段，提高边坡异常结构的识别质量，成为了重要的研究话题。本课题组针对山区公路和沿海公路场景，有机结合设备、数据、技术和交通养护部门需求，以海量无人机图像为数据基础，运用深度神经网络、视觉基础模型、多模态数据处理等手段，建立高性能道路边坡智能巡检方法。具体内容如下：(1)启动EfficientNet、Vision Transformer、ConvNeXt、Mamba等多种前沿深度神经网络，探索各类神经网络的性能边界，实现边坡落石和岩体开裂的快速定位和分类；(2)针对水库边坡场景中的少样本学习难题，使用DINOv2视觉基础模型分析RGB、红外和点云图像，编程实现特征级融合和决策级融合框架，实现边坡落石和植被侵入的快速识别；(3)基于海量无标注图像，使用自监督学习和对比学习技术探索光学-红外-点云图像之间的匹配关系，促使计算机自主发掘边坡结构之间的内在关系和发展趋势，实现多模态数据对齐，获得与巡检场景相匹配的高质量图像编码器。2. 基于开放词汇目标检测和视觉基础模型的危化品车辆识别方法从海量交通监控视频中快速发现危化品车辆，是智慧交通系统的关键环节，是提高通行效率、保障交通安全、促进绿色发展的基本前提。近年来，深度学习和计算机视觉技术成为了实现交通感知的主流方法。然而，多样的视频采集设备、繁杂的交通通行场景和稀缺的危化品车辆样例，给深度神经网络的推广应用带来了巨大挑战。为了提高泛化能力并简化训练流程，本课题组将开放词汇目标检测和视觉基础模型引入到交通视频分析领域，充分利用通用视觉特征，建立高性能危化品车辆识别方法。具体内容包括：(1)启动YOLO-World目标检测程序，合理设置关键字提示词，快速提取交通监控视频中的车辆样例。为了消除冗余信息，使用ByteTrack和SAM2技术，完成车辆的轨迹跟踪；(2)使用DINOv2视觉基础模型，提取车辆样例的视觉特征，使用UMAP和谱聚类技术理解车辆样例之间的相似性关系；(3)使用k近邻搜索去除YOLO-World的噪声样例，使用XGBoost分类器预测车辆类型，有效应对夜间、降雨等复杂场景，建立具有高泛化能力和强鲁棒性的危化品车辆识别方法。3. 孔隙结构与物理属性联合约束的非均质数字岩心建模方法3.1 基于通用图像特征的非均质岩心表征方法3.2 基于机器学习技术的三维数字岩心快速建模方法3.3 贝叶斯证据学习驱动的地质模型空间不确定性量化方法4. 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