堰塞坝表层颗粒物质智能识别的试验数据

利用无人机获取绵竹和汉中地区某堰塞坝的影像，进行预处理、影像增强、样本标注和模型训练等操作。基于深度学习技术构建多网络结构融合识别模型，利用识别模型对影像数据进行识别，得到堰塞坝表层颗粒物质的识别结果和粒径统计数据。能够为堰塞坝结构分析、危险性快速评价和应急抢险提供数据基础和决策依据。

Acquired imagery of a certain barrier dam in Mianzhu and Hanzhong regions via unmanned aerial vehicles (UAVs), followed by a series of operations including data preprocessing, image enhancement, sample annotation, and model training. A multi-network structure fused recognition model was constructed based on deep learning techniques, which was then utilized to identify the acquired imagery data, yielding recognition results of surface granular materials on the barrier dam and particle size statistical data. This dataset can provide foundational data and decision-making supports for barrier dam structural analysis, rapid hazard assessment, and emergency rescue operations.

搜集汇总

背景与挑战

背景概述

该数据集通过无人机采集绵竹和汉中地区堰塞坝影像，并基于深度学习技术构建多网络融合识别模型，实现了对表层颗粒物质的智能识别与粒径统计。它为堰塞坝的结构分析、危险性快速评价和应急抢险决策提供了关键的数据基础。