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Design of Epidemic Prevention Detection System Based on Machine Vision

机译:基于机器视觉的防疫预防检测系统设计

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An epidemic prevention detection system based on machine vision is designed in this paper. The system uses STM32F407VGT6 as the control core, uses the MLX90614 sensor to obtain the temperature, uses the average filter algorithm to filter, and performs temperature compensation on the result, which realizes the non-contact temperature measurement function. And then uses the OpenMV machine vision module as the image acquisition and processing module, uses the LBP feature recognition algorithm to detect face images, and the Haar cascade classifier extracts mask features and trains and recognizes them to realize the identity recognition and mask detection functions. Based on theoretical analysis and measured data, the absolute error value of the body temperature measurement of the epidemic prevention detection system is $leq 1.4^{circ}C$, which can accurately identify the face identity information and detect whether it meets the epidemic prevention requirements. The system involves MCU programming, image processing, face recognition and other technologies, which can help students better grasp the related knowledge of MCU programming and computer vision, cultivate students’ knowledge application ability and practical innovation ability, and meet the cultivation of innovative outstanding engineering and technical talents Requirements.
机译:本文设计了一种基于机器视觉的防疫检测系统。系统使用STM32F407VGT6作为控制核心,使用MLX90614传感器获得温度,使用平均滤波器算法过滤,并对结果进行温度补偿,从而实现了非接触式温度测量功能。然后使用OpenMV机器视觉模块作为图像采集和处理模块,使用LBP特征识别算法来检测面部图像,哈尔级联分类器提取掩码特征和列车并识别它们以实现身份识别和掩模检测功能。基于理论分析和测量数据,防疫检测系统的体温测量的绝对误差值是$ LEQ 1.4 ^ { rIC} C $,可以准确地识别面部身份信息并检测它是否符合疫情预防要求。该系统涉及MCU编程,图像处理,面部识别等技术,可以帮助学生更好地掌握MCU编程和计算机愿景的相关知识,培养学生的知识应用能力和实用创新能力,并满足创新优秀工程的培养和技术人才要求。

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