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一种基于电子邮件平台的新型智能远程监拍相机

刘昕昊 孟令军 刘峰 周小彤 王嘉诚

测试科学与仪器2025,Vol.16Issue(1):128-141,14.
测试科学与仪器2025,Vol.16Issue(1):128-141,14.DOI:10.62756/jmsi.1674-8042.2025013

一种基于电子邮件平台的新型智能远程监拍相机

A novel email-based smart remote image surveillance camera

刘昕昊 1孟令军 1刘峰 1周小彤 2王嘉诚2

作者信息

  • 1. 中北大学 仪器与电子学院,山西 太原 030051
  • 2. 上海大学 机电工程与自动化学院,上海 200444
  • 折叠

摘要

Abstract

Aiming at the problems of difficult deployment and access of surveillance system server,as well as high operation and maintenance cost,a remote surveillance camera is designed based on RK3566 chip,which is controlled and transmits data via email platform.Firstly,to address the impact of environmental factors such as weather and light on image quality,a deep neural network(DNN)image exposure correction network is employed to rectify images with abnormal exposure.Additionally,a back propagation(BP)neural network is utilized to fit a curve relating the brightness difference to the gamma value of images before and after exposure correction,thereby adjusting the gamma value of the camera.Secondly,to enhance the precision of YOLOv5 algorithm in differentiating between anomalies in nighttime imagery,infrared image data are employed,and a context-aware light-weight label assignment head and coordinate attention mechanism are incorporated into the model to augment the model's detection accuracy and recall rate for small targets.Furthermore,to meet the demand for reporting of abnormal situations in unattended environments,an automatic target identification and reporting process has been designed which combines YOLOv5 algorithm with the frame-difference motion detection algorithm.The camera has been tested for compatibility with the current mainstream commercial email platforms.The mean time required for transmitting a single image file via the email platform is less than 10 s,while the mean time for transmitting a short video is less than 60 s.The BP network's average training loss is 0.015,and the average testing loss is 0.013,which basically meets the precision requirements for gamma adjustment.The improved YOLOv5 algorithm achieved an mAP@0.5 of 91.5%and a recall rate of 85.5%,effectively enhancing the accuracy of small object detection.

关键词

邮件传输/曝光修正/反向传播神经网络/gamma值/YOLOv5

Key words

email transmission/exposure correction/back propagation(BP)neural network/gamma value/YOLOv5

引用本文复制引用

刘昕昊,孟令军,刘峰,周小彤,王嘉诚..一种基于电子邮件平台的新型智能远程监拍相机[J].测试科学与仪器,2025,16(1):128-141,14.

基金项目

This work was Funded by Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University,China(No.GZKF-202219). (No.GZKF-202219)

测试科学与仪器

1674-8042

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