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作 者:陈娟 陈东升 李晓宁[2] 蒋传健 CHEN Juan;CHEN Dong-Sheng;LI Xiao-Ning;JIANG Chuan-Jian(School of Mathematics and Computer Science,Chongqing College of International Business and Economics,Chongqing 401520,China;School of Computer Science,Sichuan Normal University,Chengdu 610000,China)
机构地区:[1]重庆对外经贸学院数学与计算机学院,重庆401520 [2]四川师范大学计算机科学学院,成都610000
出 处:《四川大学学报(自然科学版)》2024年第3期152-160,共9页Journal of Sichuan University(Natural Science Edition)
基 金:重庆市教委科学技术研究项目(KJQN202302004);重庆市教委科学技术研究项目(KJZD-K202202001);重庆对外经贸学院科学研究项目(KYZK202310)。
摘 要:针对儿童高空坠楼问题,本文利用目标检测与目标测距技术设计了一种高空坠楼监测预警系统.系统改善了现有YOLO目标检测算法模型过大参数量过多的问题,同时还借助单目摄像头测距方法设计了一种新的危险距离测量算法.实验结果表明,改进后的目标检测算法在保证其他参数相差不大的情况下,model size与比原来的YOLOv8n降低了1.99 MB,parameters比原始模型降低了1.039×109.新设计的危险距离测量算法在4个不同场景100组数据中横向误差均值为2.29 cm,纵向误差均值为3.16 cm,危险监测预警准确率92%.综合说明,本文设计的高空坠楼检测预警系统准确性高、实用性强,有较高的社会应用价值.This paper addresses the issue of children falling from heights,by proposing a monitoring and warning system that utilizes target detection and ranging technologies to mitigate high-altitude falls.The system has improved upon the existing challenges posed by large parameter sizes in the YOLO object detection algorithms,and introduced a new hazard distance measurement algorithm based on monocular camera ranging techniques.The experimental results indicate that the improved object detection algorithm,while maintaining similar performance in other parameters,reduces the model size by 1.99 MB and the number of parameters by 1.039 billion compared to the original YOLOv8n model.The newly designed hazard distance measurement algorithm achieved a mean lateral error of 2.29 cm,a mean longitudinal error of 3.16 cm and a hazard monitoring warning accuracy of 92.00%across 100 data sets in four different scenarios.In summary,the high-altitude falling detection and early warning system presented in this paper exhibits high accuracy and robust practicality,making it highly valuable for societal applications.
关 键 词:高空坠楼 儿童安全监测 目标检测 距离测算 危险预警
分 类 号:TP277[自动化与计算机技术—检测技术与自动化装置]
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