通信与信息技术Issue(3)：5-11,17,8.
基于视频异常理解的实验室不安全行为检测方法
A novel method of unsafe behavior detection in chemical laboratories with video anomaly understanding
摘要
Abstract
Due to the presence of advanced equipment and chemical reagents in the chemistry laboratory,identifying unsafe behav-iors in the laboratory has become extremely complex.Accurately identifying and understanding unsafe behaviors in videos is a difficult problem for laboratory safety.Firstly,a dataset of laboratory behavior is constructed,and abnormal behaviors are annotated in detail based on VLM-Deepseek,including multiple-choice QA,anomaly types,temporal annotations,and inference chains.We use attention to ex-tract the positions of people and objects,and then performing spatiotemporal feature extraction.Three sets of such structures parallelized to improve extraction efficiency.Finally,we propose the understanding model of unsafe behavior based on GRPO,and different reward rules are formulated for reinforcement learning tasks includes multiple-choice QA,inference and classification.The model analyzes the cause of unsafe behaviors and gives reasonable explanations.The experimental results show that the accuracy of the spatiotemporal fea-ture extraction model is 96.1,and the classification accuracy based on the GRPO model is 75.21.It can perform more accurate behavior analysis and abnormal time localization,providing effective reference for intelligent management of chemical laboratory risks.关键词
视频异常理解/不安全行为检测/大语言模型/并行深度结构Key words
Video anomaly understanding/Unsafe behavior detection in laboratories/A big language model/Parallel structures to ex-tract features分类
信息技术与安全科学引用本文复制引用
王珺琳,钮天浩..基于视频异常理解的实验室不安全行为检测方法[J].通信与信息技术,2026,(3):5-11,17,8.基金项目
辽宁省教育厅高等学校基本科研项目青年项目(项目编号:1030040000668)沈阳理工大学引进高层次人才项目(项目编号:1010147001228) （项目编号:1030040000668）
