基于计算机视觉的个人防护装备识别应用研究与实现

Research and implementation of personal protective equipment recognition application based on computer vision

在工业和建筑等高风险环境中,个人防护装备(PPE)的使用对于保障工人安全至关重要。然而,传统的PPE合规性检查通常依赖于人工监控,效率低下且容易出错。对此,文章提出了一种基于计算机视觉的自动化PPE检测系统,旨在提高检测的准确性和效率。该系统采用YOLOv5s深度学习模型,结合大量标注图像数据集,可对安全头盔、反光背心、防护眼镜、手套等PPE进行实时检测。通过数据增强技术和迁移学习,显著提升了模型的检测性能,为PPE检测提供了一种高效、可靠的解决方案,具有广阔的应用前景。

The use of personal protective equipment(PPE)is crucial for ensuring worker safety in high risk environments such as industry and construction.However,traditional PPE compliance checks often rely on manual monitoring,which is inefficient and prone to errors.The article proposes an automated PPE detection system based on computer vision,aiming to improve the accuracy and efficiency of detection.The system uses the YOLOv5s deep learning model,combined with a large annotated image dataset,to perform real time detection of PPE such as safety helmets,reflective vests,protective goggles,gloves,etc.Through data augmentation techniques and transfer learning,the article significantly improves the detection performance of the model,providing an efficient and reliable solution for PPE detection with broad application prospects.