实验室智能安防管理系统的设计与实现

Design and implementation of an intelligent security management system for laboratories

摘要

实验室作为教学科研与学生实训的主要场所,其安全至关重要.设计1款实验室智能安防管理系统,针对以往的实验室监管系统存在盲区、滞后、误报等问题,采用轻量化 YOLOv7-tiny+CBAM 与双流自适应融合网络的分层检测架构,通过数据增强、迁移学习与剪枝量化加速三级优化,分别提升环境适应性、样本利用效率和边缘部署性能,实现对实验室安全隐患的精准识别与处置.该系统为实验室安全管理提供了可复制的智能化解决方案,同时对高危场景下的智能监控具有推广价值.

Abstract

As the crucial sites for teaching and research as well as students' practical training, the security of the laboratory is of vital importance. An intelligent security management system for laboratories was designed to address issues in conventional systems, such as monitoring blind spots, delayed responses, and false alarms. This system adopts a lightweight hierarchical detection architecture based on YOLOv7-tiny with CBAM and a dual-stream adaptive fusion network. Through three-level optimization—data augmentation, transfer learning, and pruning-quantization-acceleration—it enhances environmental adaptability, sample utilization efficiency, and edge deployment performance respectively, achieving precise identification and handling of laboratory safety hazards. This system provides a replicable intelligent solution for laboratory safety management and has promotional value for intelligent monitoring in high-risk scenarios.

关键词

实验室安全 / 计算机视觉 / 深度学习 / 闭环管理 / 智能监控

Key words

laboratory safety / computer vision / deeper learning / closed-loop management / intelligent surveillance

参考文献

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