建筑工地安全环境识别分析数据

该数据主要用来捕捉工地上对于未佩戴安全帽、吸烟、打架、未穿发光背心等违规行为，如发现违规行为会马上产生告警，可以及时保证制止违规行为，保证工地文明施工和安全施工。后续也可以通过告警数据分析，可以对违规率超标的工地进行处罚和安全教育，加强工地安全保障。安全帽检测算法：使用目标检测算法，如YOLO或Faster R-CNN，对图像或视频中的人员进行检测。对检测到的人员区域进行特征提取，如使用卷积神经网络（CNN）提取人脸区域。利用分类器或深度学习模型判断人员是否佩戴安全帽。训练这样的模型需要一组带有标注的图像数据集。
吸烟检测算法：利用目标检测算法检测图像或视频中的人员。对检测到的人员区域进行特征提取，如利用CNN提取人脸区域。使用分类器或深度学习模型判断人员是否吸烟。同样，这需要训练模型的图像数据集。
打架检测算法：使用行为识别算法，如长短时记忆网络（LSTM）或卷积神经网络（CNN）对图像序列或视频进行分析。提取图像序列中的人员姿态、动作等特征。使用分类器或深度学习模型判断是否存在打架行为。同样，这需要训练模型的图像、视频数据集。
发光背心检测算法：利用目标检测算法检测工地上的人员。对检测到的人员区域进行特征提取，如利用CNN提取人脸区域，并进一步判断是否穿着发光背心。使用分类器或深度学习模型进行背心佩戴判断。同样，训练这样的模型需要带有标注的图像数据集。

This dataset is primarily developed to capture common safety violations on construction sites, including failure to wear safety helmets, smoking, fighting, and not wearing luminous vests. Once a violation is detected, an immediate alarm will be triggered to stop the violation in time, ensuring civilized and safe construction. In addition, through subsequent analysis of alarm data, penalties and safety education can be carried out for construction sites with excessive violation rates, so as to strengthen site safety guarantees.

Safety Helmet Detection Algorithm: Object detection algorithms such as YOLO or Faster R-CNN are used to detect personnel in images or videos. Feature extraction is performed on the detected personnel regions, for example, extracting facial regions using Convolutional Neural Networks (CNNs). A classifier or deep learning model is utilized to determine whether the personnel are wearing safety helmets. Training such a model requires a set of labeled image datasets.

Smoking Detection Algorithm: Object detection algorithms are used to detect personnel in images or videos. Feature extraction is conducted on the detected personnel regions, such as extracting facial regions via CNNs. A classifier or deep learning model is applied to judge whether the personnel are smoking. Similarly, an image dataset for model training is required.

Fighting Detection Algorithm: Behavior recognition algorithms such as Long Short-Term Memory Networks (LSTM) or Convolutional Neural Networks (CNNs) are used to analyze image sequences or videos. Features including personnel posture and movements are extracted from the image sequences. A classifier or deep learning model is used to determine whether fighting behaviors exist. Likewise, labeled image and video datasets are needed for model training.

Luminous Vest Detection Algorithm: Object detection algorithms are used to detect personnel on construction sites. Feature extraction is performed on the detected personnel regions, such as extracting facial regions using CNNs, and further determining whether the personnel are wearing luminous vests. A classifier or deep learning model is used to judge the wearing status of the luminous vest. Similarly, training such a model requires labeled image datasets.

搜集汇总

数据集介绍

特点

该数据集是建筑工地安全环境识别分析数据，包含1001条记录，用于检测工地违规行为如未戴安全帽、吸烟等，并通过告警系统保障施工安全。数据来源于企业，已在浙江省知识产权区块链公共存证平台存证。