基于机器视觉和轻量化 YOLO 的展品识别系统设计
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1.湖南开放大学智能制造学院 长沙 410004; 2.湖南大学电气与信息工程学院 长沙 410082

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TP311.52; TN911.73

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湖南省自然科学基金面上项目(2023JJ30146)、湖南开放大学研究项目(XDK-2025-C-50)资助


Design of an exhibit recognition system based on computer vision and a lightweight YOLO model
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1.College of Intelligent Manufacturing, Hunan Open University,Changsha 410004, China; 2.College of Electrical and Information Engineering,Hunan University,Changsha 410082, China

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    摘要:

    为了增强博物馆等展厅的科技感、互动性,提升展厅服务水平,设计了一种基于机器视觉技术,具有导航定位、展品智能化讲解等功能的识别系统。硬件平台以树莓派作为主控,外接摄像头,红外传感器等感知模块。摄像头采集图像后通过图像处理算法识别路径信息控制车体运动。展厅中的展品采用剪枝,量化后的YOLO模型检测,测试结果表明,通道保留率为40%时,模型参数减少了18.09%,浮点运算数减少了12.35%,测试集精度依然可达98.5%。设计的系统可以准确识别引导线路、交叉线和展品信息,部署方便具有很好的推广价值。

    Abstract:

    To enhance the technological feel and interactivity of exhibition halls such as museums, and to improve the service level of these halls, this study designs an recognition system based on computer vision technology, featuring functions such as navigation and positioning, as well as intelligent audio guide for exhibits. The system is mainly controlled by Raspberry Pi, with external cameras, infrared remote sensors and other hardware modules. After the image is collected by the camera of the guide vehicle, the path information is identified by the image processing algorithm to control the movement of the vehicle body. The test results showed that when the channel retention rate was 40%, the model parameters were reduced by 18.09%, the floating-point operations were reduced by 12.35%, and the test set accuracy still reached 98.5%. The designed system can accurately identify guide lines, cross lines, and exhibit information, and is easy to deploy and has great promotional value.

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柴世杰,全惠敏.基于机器视觉和轻量化 YOLO 的展品识别系统设计[J].电子测量技术,2026,49(10):35-42

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  • 在线发布日期: 2026-08-25
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