SH17：面向制造业人体安全与个人防护装备检测的数据集
SH17: A Dataset for Human Safety and Personal Protective Equipment Detection in Manufacturing Industry
AI总结：
本文提出SH17数据集，包含8,099张工业图像和75,994个PPE实例，用于训练目标检测模型，并以YOLOv9-e超过70.9%的准确率验证了其在制造业人体安全与PPE合规检测中的有效性。
摘要：
工作场所事故持续对人体安全构成重大风险，尤其是在建筑和制造等行业，有效的个人防护装备（PPE）合规性需求已变得愈发重要。我们的研究聚焦于开发基于目标检测（OD）和卷积神经网络（CNN）的非侵入式技术，以检测和验证各类PPE的正确使用情况，如安全帽、护目镜、口罩和防护服。本研究提出了SH17数据集，该数据集包含8,099张标注图像，涵盖来自多样化工业环境的17个类别的75,994个实例，用于训练和验证目标检测模型。我们训练了最先进的目标检测模型进行基准测试，初步结果表明，You Only Look Once（YOLO）v9-e模型变体在PPE检测中取得了超过70.9%的准确率。模型在跨域数据集上的验证性能表明，整合这些技术可以显著改进安全管理系统，为努力满足人体安全法规并保护其劳动力的行业提供可扩展且高效的解决方案。该数据集可在https://github.com/ahmadmughees/sh17dataset获取。
英文摘要：
Workplace accidents continue to pose significant risks for human safety, particularly in industries such as construction and manufacturing, and the necessity for effective Personal Protective Equipment (PPE) compliance has become increasingly paramount. Our research focuses on the development of non-invasive techniques based on the Object Detection (OD) and Convolutional Neural Network (CNN) to detect and verify the proper use of various types of PPE such as helmets, safety glasses, masks, and protective clothing. This study proposes the SH17 Dataset, consisting of 8,099 annotated images containing 75,994 instances of 17 classes collected from diverse industrial environments, to train and validate the OD models. We have trained state-of-the-art OD models for benchmarking, and initial results demonstrate promising accuracy levels with You Only Look Once (YOLO)v9-e model variant exceeding 70.9% in PPE detection. The performance of the model validation on cross-domain datasets suggests that integrating these technologies can significantly improve safety management systems, providing a scalable and efficient solution for industries striving to meet human safety regulations and protect their workforce. The dataset is available at https://github.com/ahmadmughees/sh17dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
