基于RetinaNet的安全帽检测系统设计

摘要生产安全大于天，安全帽作为一种个人防护装备，在工业生产中起着至关重要的作用。佩戴安全帽可以有效地保护人员头部免受外界因素的伤害，如坠落物、打击、热辐射等。然而，由于人为因素和其他原因，缺乏对佩戴安全帽的有效监控，存在着工伤事故的隐患。因此，开发一种高效准确的安全帽识别系统，可以提高工业安全水平，减少工伤事故的发生。近年来，深度学习技术迅速发展，成为计算机视觉领域的重要研究方向。传统的安全帽检测方法依赖于手工设计的特征提取器和分类器，需要花费大量时间和精力。而深度学习通过端到端的学习方法，可以直接从图像数据中学习到表示特征和分类模型，自动地进行特征提取和分类任务。因此，利用深度学习技术进行安全帽识别，具有很高的可行性和应用价值。本文基于RetinaNet模型实现了一个安全帽识别系统。首先构建了一个包含两个类别的安全帽数据集，包括戴帽和不戴帽的情况。使用RetinaNet模型进行训练和测试，测试结果表明，相比于传统的安全帽识别方法，该系统具有更高的识别准确度和鲁棒性，在清晰图片下能够准确地检测出佩戴安全帽的情况，但在姿态变化、遮挡和多人头等复杂环境下，其识别性能有所下降。总体而言，本设计基本满足安全帽检测任务中的实时性和准确率的要求。关键词：深度学习安全帽识别卷积神经网络RetinanetDesign of safety helmet detection system based on RetinaNetYe Mingyang(College of Electronic Engineering/College of Artificial Intelligence,South China AgriculturalUniversity,Guangzhou 510642,China)Abstract:Safety comes before everything,and safety helmets play a critical role in personalprotective equipment in industrial production.Wearing safety helmets can effectively protectthe head from external factors such as falling objects,impacts,and heat radiation.However,due to human factors and other reasons,there is a hidden danger of occupational injuries due toa lack of effective monitoring of safety helmet-wearing.Therefore,developing an efficient andaccurate safety helmet recognition system can improve industrial safety levels and reduceoccupational accidents.In recent years,deep learning technology has rapidly developed and become an importantresearch direction in computer vision.Traditional safety helmet detection methods rely onmanually designed feature extractors and classifiers,which require a lot of time and effort.Deep learning,through end-to-end learning methods,can directly learn representation featuresand classification models from image data,automatically performing feature extraction andclassification tasks.Therefore,using deep learning technology for safety helmet recognitionhas high feasibility and application value.This article implements a safety helmet recognition system based on the RetinaNet model.First,a safety helmet dataset containing two categories,wearing helmets and not wearinghelmets,was constructed.The RetinaNet model was then used for training and testing,and thetest results showed that,compared with traditional safety helmet recognition methods,thesystem has higher recognition accuracy and robustness,and can accurately detect the wearingof safety helmets in clear images.However,its recognition performance decreases in complexenvironments such as pose changes,occlusion,and multiple heads.Overall,this design meetsthe requirements of real-time and accuracy in safety helmet detection tasks.Key words:Deep learning Safety helmet recognition CNN RetinaNet目录1前言.1.1课题的研究背景1.2课题国内外研究现状和发展趋势1.3研究目的和意义.21.4章节安排….22相关理论介绍.0.22.1深度卷积神经网络.22.2 RetinaNet的结构和原理.62.2.1 RetinaNet简介..62.2.2网络组成.62.3本章总结.93系统设计93.1系统框架.93.2配置开发环境.103.3制作安全帽数据集.103.4本章总结.124训练RetinaNet模型.124.1模型训练124.2结果分析.….124.3本章总结.135系统测试.135.1模型在不同情形下的检测效果.135.2模型对比..145.3本章总结.156总结展望15参考文献..16致谢.17华南农业大学本科生毕业论文（或设计）成绩评定表