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.

翻译：工作场所事故持续对人员安全构成重大风险，尤其在建筑和制造等行业，对有效遵守个人防护装备（PPE）规范的需求变得日益重要。本研究致力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，以检测和验证头盔、护目镜、口罩及防护服等多种PPE的正确使用。本文提出了SH17数据集，该数据集包含从多样化工业环境中采集的8,099张标注图像，涵盖17个类别的75,994个实例，用于训练和验证OD模型。我们训练了先进的目标检测模型进行基准测试，初步结果表明其具有较高的准确率，其中YOLOv9-e模型变体在PPE检测中超过70.9%。在跨域数据集上的模型验证性能表明，整合这些技术可显著提升安全管理体系，为致力于满足人员安全法规并保护劳动力的行业提供可扩展的高效解决方案。数据集可通过https://github.com/ahmadmughees/sh17dataset获取。