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小榄网站,重庆网站备案需要几天,成都品牌推广,沈阳营销型网站摘要 随着建筑行业的发展#xff0c;施工现场的安全管理问题日益突出#xff0c;如何实时有效地识别施工现场的危险源#xff0c;确保工人安全#xff0c;成为亟待解决的技术难题。本论文提出了一种基于机器视觉的施工场景危险源识别系统#xff0c;利用目标检测算法YOLO和…摘要随着建筑行业的发展施工现场的安全管理问题日益突出如何实时有效地识别施工现场的危险源确保工人安全成为亟待解决的技术难题。本论文提出了一种基于机器视觉的施工场景危险源识别系统利用目标检测算法YOLO和深度学习框架ncnn结合边缘计算设备旨在提升施工现场安全管理的智能化水平。系统通过实时采集施工现场的图像数据使用YOLO对危险源进行检测与识别并通过预警机制通知相关人员进行干预从而降低安全事故发生的概率。论文首先构建了一个专门针对施工现场的危险源数据集。接着针对深度学习模型的计算效率问题本文对YOLOv8模型进行了优化从而提高了系统的实时性和运行效率。然后系统部署在安卓手机实现了低延迟的实时识别功能。在实验部分本论文验证了所提出系统在准确率、实时性和鲁棒性方面的优势。实验结果表明本系统不仅能够在多种施工环境中稳定运行还能在复杂条件下准确识别多种危险源。最后本论文总结了系统的创新点和不足之处并对后续研究进行了展望。未来的研究将重点在于扩展系统的多场景适应性、提高检测精度和智能化水平并在实际施工现场中进行广泛应用以进一步提升建筑施工的安全性和效率。关键词机器视觉施工安全危险源识别YOLO边缘计算AbstractWith the development of the construction industry, safety management issues at construction sites have become increasingly prominent. How to effectively identify hazards at construction sites in real time and ensure worker safety has become an urgent technical problem to be solved. This paper proposes a construction scene hazard identification system based on machine vision, which uses the target detection algorithm YOLO and the deep learning framework ncnn, combined with edge computing equipment, to improve the intelligent level of construction site safety management. The system collects real-time image data of the construction site, uses YOLO to detect and identify hazards, and notifies relevant personnel to intervene through an early warning mechanism, thereby reducing the probability of safety accidents.The paper first constructed a specialized dataset of hazardous sources for construction sites. Furthermore, to address the issue of computational efficiency in deep learning models, this paper optimized the YOLOv8 model, thereby improving the real-time performance and operational efficiency of the system. Then, the system was deployed on Android phones, achieving low latency real-time recognition functionality.In the experimental section, this paper validates the advantages of the proposed system in terms of accuracy, real-time performance, and robustness. The experimental results show that this system can not only operate stably in various construction environments, but also accurately identify multiple hazards under complex conditions.Finally, this paper summarizes the innovative points and shortcomings of the system, and provides prospects for future research. Future research will focus on expanding the system’s multi scenario adaptability, improving detection accuracy and intelligence level, and widely applying them in actual construction sites to further enhance the safety and efficiency of building construction.目录第1章 引言 31.1研究背景和意义 31.2国内外研究现状 41.3主要内容和工作安排 5第2章 相关理论与技术 72.1目标检测算法YOLO 72.2深度学习框架ncnn 82.3Android Studio 82.4本章小结 8第3章 施工场景危险源识别系统设计 93.1需求分析 93.2系统总体架构 103.3系统设计阶段 113.4可行性研究 143.5工程管理 163.6本章小结 18第4章 模型训练 194.1数据集的构建 194.2模型训练与优化 204.3本章小结 24第5章 系统实现 255.1移动端部署 255.2后台部署 275.3后端部署 315.4本章小结 32第6章 总结与展望 336.1 主要工作与创新点 336.2后续研究工作展望 346.3总结 36参考文献 37致谢 39