摘要
该文面向本科生与研究生实践教学,利用计算机视觉技术开发了工业人员行为分析实验平台。依托工程实训中心,通过多通道数据采集装置收集人员工作环境、作业行为以及劳保用品等数据。基于PyQt5构建工业人员行为分析实验平台,该平台将计算机视觉技术与工业安全管控标准深度融合,以实现工业人员流程类与非流程类作业的行为分析。为验证实验平台的有效性,使用工业人员行为测试数据对所研发的实验平台进行了实验验证。实验结果表明,该实验平台能对工人多种作业进行有效评判。该实验平台有助于提升本科生和研究生在智能图像处理领域的实践能力。
[Objective]Computer vision technology has been widely used and has achieved satisfactory recognition results in industrial safety.However,most studies mainly focus on the static identification of a personnel’s protective wear or noncontinuous unsafe behaviors,and they fail to integrate and apply artificial intelligence technology with industrial safety control standards.This paper develops an experimental platform to analyze the behavior of industrial personnel using computer vision technology.The platform is integrated with industrial safety control standards for the practical teaching of undergraduate and postgraduate students.[Methods]The function structure of the experimental platform includes the resource,algorithm,and application layers.The resource layer relies on the engineering training center,in which multichannel data acquisition devices are employed to collect data on the work environment,worker operations,and labor protection appliances.The algorithm layer implements the identification and analysis of an industrial personnel’s unsafe behaviors using computer vision algorithms involving human body key point detection and behavior recognition.The application layer provides the platform with diverse functions,including the analysis of a personnel’s unsafe behaviors and behavioral data archiving.Moreover,intelligent control methods facilitate the integration with the algorithm layer,enabling the customization of application services according to the specific requirements at the algorithm level.This process also ensures the monitoring of enterprise-wide production processes for safety purposes.The industrial worker behavior analysis experimental platform is designed and built using PyQt5 and incorporates modules for human–computer interaction,data input,personnel behavior analysis,and data storage.The human–computer interaction module implements the platform’s function control and data visualization,facilitating interaction between various sectors through function buttons.Further,the interaction module generates keyframe images or videos in the designated display area while concluding the personnel behavior analysis and concurrently outputs text descriptions of the analysis results through the analysis result display area.The data input module collects and transmits behavioral data.The personnel behavior analysis module employs various algorithmic models to analyze and process the video data from the data input module according to the specific requirements of different industrial scenarios.The data storage module manages the storage and retrieval of identity information for industrial personnel,behavioral video data,behavioral analysis results,and corresponding keyframe images.[Results]In the verification phase of the experimental platform,the operational behavior analysis of the personnel was performed.The experimental results demonstrate that the platform supports various safe work behavior analysis algorithms and enables automated identification of an industrial personnel’s work behavior throughout the video capture process,according to industry safety control standards.This capability enables the effective assessment of diverse worker operations.[Conclusions]The proposed experimental platform can help enhance the intelligent image processing skills of undergraduate and postgraduate students.Moreover,it can serve as a reference for designing related industrial safety monitoring systems,improving the level of industrial safety,and offering potential practical applications.
作者
徐晓滨
孔俊杰
张泽辉
王坚
陈龙
何宏
XU Xiaobin;KONG Junjie;ZHANG Zehui;WANG Jian;CHEN Long;HE Hong(China-Austria Belt and Road Joint Laboratory on Artificial Intelligence and Advanced Manufacturing,Hangzhou Dianzi University,Hangzhou 310018,China;School of Automation,Hangzhou Dianzi University,Hangzhou 310018,China;HDU-ITMO Joint Institute,Hangzhou Dianzi University,Hangzhou 310018,China;School of Electronics and Information Engineering,Hangzhou Dianzi University,Hangzhou 310018,China;School of Communication Engineering,Hangzhou Dianzi University,Hangzhou 310018,China)
出处
《实验技术与管理》
CAS
北大核心
2024年第9期101-110,共10页
Experimental Technology and Management
基金
浙江省“尖兵”“领雁”研发攻关计划项目(2024C03254)
国家重点研发计划项目(2022YFE0210700)
浙江省自然科学基金资助项目(LTGG24F030004)
国家水运安全工程技术研究中心开放基金项目(A202403)
宁东能源化工基地本级重点支持领域科技项目(2023NDKJXMLX059)。
关键词
计算机视觉
工业安全
行为分析
人体关键点检测
computer vision
industrial safety
behavior analysis
human key point detection
