绑扎钢筋阶段识别模型数据

基于深度学习的施工进度识别技术，通过高度智能化的数据处理与分析，得到绑扎钢筋施工阶段识别模型数据集合，为建筑行业带来了革命性的管理变革，增强了施工过程的透明度与可控性，显著提升了项目的安全性、效率和经济效益。
"1.数据采集：采集海量不同施工阶段的多样化现场场景数据，为模型训练提供了丰富的真实数据基础。
2.数据处理：
（1）数据预处理：首先对基于多任务联合优化的工程进度识别模型训练进行预处理，然后进行特征提取与模型构建：
（2）特征提取：采用ResNet50作为深度学习模型的骨干网络，该网络能高效提取图像的深层特征，为每个输入数据生成2048维的特征表达。
（3）分类器设计：在特征表达基础上添加分类器层，将特征向量映射至22个类别的判别向量，并取22个值中的最大值所对应的类别作为模型最终的预测类别，实现对施工阶段的精确分类。
3.数据应用：创新性地同时利用交叉熵分类损失函数和基于特征相应图的目标定位损失函数联合优化模型参数，使其达到更佳的工程进度识别效果，得到施工进度识别数据集合及其出的模型，显著提升工程项目的管理效率和安全性，有效控制成本、保证工程质量，大幅改善建筑工程管理，是推动建筑行业数字化转型的关键数字化技术之一。
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Deep learning-based construction progress recognition technology, through highly intelligent data processing and analysis, generates a dataset for the rebar tying construction phase recognition model. This technology has brought a revolutionary management transformation to the construction industry, enhanced the transparency and controllability of the construction process, and significantly improved the safety, efficiency and economic benefits of construction projects.

1. Data Collection: Collect massive volumes of diverse on-site scene data across different construction phases, providing a rich real-world data foundation for model training.

2. Data Processing:
(1) Data Preprocessing: First, preprocess the training of the engineering progress recognition model optimized via multi-task joint optimization, then proceed to feature extraction and model construction:
(2) Feature Extraction: Adopt ResNet50 as the backbone network of the deep learning model, which can efficiently extract deep features of images and generate 2048-dimensional feature representations for each input data sample.
(3) Classifier Design: Add a classifier layer based on the feature representations, map the feature vectors to discriminant vectors of 22 categories, and select the category corresponding to the maximum value among the 22 values as the final predicted category of the model, thereby achieving accurate classification of construction phases.

3. Data Application: Innovatively, jointly optimize the model parameters using both the cross-entropy classification loss function and the target localization loss function based on feature response maps to achieve better performance in engineering progress recognition. The resulting construction progress recognition dataset and derived model can significantly improve the management efficiency and safety of engineering projects, effectively control costs, ensure engineering quality, greatly enhance construction project management, and stand as one of the key digital technologies promoting the digital transformation of the construction industry.

搜集汇总

数据集介绍

特点

该数据集为绑扎钢筋阶段识别模型数据，由三家公司联合申请，包含2049条数据，用于建筑行业的施工进度识别。通过深度学习技术，特别是ResNet50网络，实现施工阶段的精确分类，提升工程管理效率和安全性。