视频抄表无线通讯系统
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摘要
仪器仪表被广泛地应用于各行各业的测量系统中,由于某些仪器仪表并没有数据传送的接口,对其进行现场数据读取既浪费人力资源又难以避免人为因素的干扰,因此本系统要实现仪表数字的远程读取。系统的实现需要利用视频技术、图像处理技术、图像识别技术以及无线传输技术。
基于现代抄表的现状以及发展趋势,采用数字识别的方法,使用ARM系统搭建软硬件平台,把采集的图像数据按照GPRS网络协议要求打包,采用MA8-9i模块发送到处理中心,处理中心计算机采用BP神经网络实现数字识别。采用带有USB接口的摄像头进行图像采集,省去采集卡,降低了成本,采用ARMSYS2410开发板把采集装置的体积减小化。对仪表的数字图像,运用Visual C++开发工具实现了图像锐化、图像去噪、图像分割、图像特征提取等图像预处理的相关算法。在图像二值化方面,采用了给定阈值的二值化方法,并给出了处理结果。图像的锐化采用了梯度锐化的方法,可以使图像模糊的边缘变得清晰。图像噪声的去除采用的是去除离散杂点的方法,有效的去除了噪声。采用改进边缘检测算法来分割图像,取得了良好的效果。采用13点特征提取法提取数字特征,并加以保存以便送给BP网络进行训练,BP神经网络对于Arial字体的数字可以达到90%以上的识别,提高了识别率。系统减少了对仪表读数的人为因素干扰,也达到远程抄表的目的。
本系统集采集、无线传输和图像识别于一体,实现了对仪表数字的远程读取功能,具有实用价值。最后,对本文的工作进行了总结,提出了存在的问题和进一步改进的方向。
Instruments are widely applied in the way of the measurement system for many fields. It is a waste of the human resources to read the data of the instruments. The interference of the human factors cannot be avoided during the measurement. The reason is that some instruments have no interface to send the data. So, the purpose of this system is to realize the remote reading. The system will be realized with the video technique, the image processing technique, the image recognition technique and the wireless transmission technique.
The dissertation is based on the current situation and the exploitation trend of the modern meter reading and the digital recognition method. The ARM system was used to build the software and the hardware platform and to pack the collected data according to the GPRS network protocol. The MA8-9i module can be used to transmit the data to the processing center. The BP neural network is used to realize the digital recognition. In this dissertation, the USB camera with the image is adopted to save the acquisition card and cut down the cost, the ARMSYS2410 exploitation board is used to reduce the volume of the acquisition device. In order to deal with the digital image of the measuring appliance, the Visual C++ exploitation device is adopted to realize the relevant algorithm of the image pretreatment such as the image sharpening, the image denoising, the image segmentation, and the extraction of the image characteristics. The processing results were given by using of the threshold method in respect of the image binaryzation. The method of gradient sharpening method is used to make the fuzzy edge change to clear. The method of removing the discrete mixed spot is used to remove the image noise. The method that improves the edge examination algorithm is utilized to segment the image and the good effect has obtained. The 13 points characteristics extraction method is employed to obtain and save the digital characteristics in order to send them to the BP network to take the training. The recognition of the BP neural network for the Arial typeface may reach up to 90%, which has improved the recognition rate. This system can reduce the interference of the human factors for the meter reading, and it also can achieve the purpose of remote meter reading.
The system is the integration of the sampling, the wireless transmission and the image recognition. It realizes the function of the remote meter reading and has the practical value. In the last content in the dissertation, the work of dissertation was summarized, and the ameliorative direction was also proposed.
基于现代抄表的现状以及发展趋势,采用数字识别的方法,使用ARM系统搭建软硬件平台,把采集的图像数据按照GPRS网络协议要求打包,采用MA8-9i模块发送到处理中心,处理中心计算机采用BP神经网络实现数字识别。采用带有USB接口的摄像头进行图像采集,省去采集卡,降低了成本,采用ARMSYS2410开发板把采集装置的体积减小化。对仪表的数字图像,运用Visual C++开发工具实现了图像锐化、图像去噪、图像分割、图像特征提取等图像预处理的相关算法。在图像二值化方面,采用了给定阈值的二值化方法,并给出了处理结果。图像的锐化采用了梯度锐化的方法,可以使图像模糊的边缘变得清晰。图像噪声的去除采用的是去除离散杂点的方法,有效的去除了噪声。采用改进边缘检测算法来分割图像,取得了良好的效果。采用13点特征提取法提取数字特征,并加以保存以便送给BP网络进行训练,BP神经网络对于Arial字体的数字可以达到90%以上的识别,提高了识别率。系统减少了对仪表读数的人为因素干扰,也达到远程抄表的目的。
本系统集采集、无线传输和图像识别于一体,实现了对仪表数字的远程读取功能,具有实用价值。最后,对本文的工作进行了总结,提出了存在的问题和进一步改进的方向。
Instruments are widely applied in the way of the measurement system for many fields. It is a waste of the human resources to read the data of the instruments. The interference of the human factors cannot be avoided during the measurement. The reason is that some instruments have no interface to send the data. So, the purpose of this system is to realize the remote reading. The system will be realized with the video technique, the image processing technique, the image recognition technique and the wireless transmission technique.
The dissertation is based on the current situation and the exploitation trend of the modern meter reading and the digital recognition method. The ARM system was used to build the software and the hardware platform and to pack the collected data according to the GPRS network protocol. The MA8-9i module can be used to transmit the data to the processing center. The BP neural network is used to realize the digital recognition. In this dissertation, the USB camera with the image is adopted to save the acquisition card and cut down the cost, the ARMSYS2410 exploitation board is used to reduce the volume of the acquisition device. In order to deal with the digital image of the measuring appliance, the Visual C++ exploitation device is adopted to realize the relevant algorithm of the image pretreatment such as the image sharpening, the image denoising, the image segmentation, and the extraction of the image characteristics. The processing results were given by using of the threshold method in respect of the image binaryzation. The method of gradient sharpening method is used to make the fuzzy edge change to clear. The method of removing the discrete mixed spot is used to remove the image noise. The method that improves the edge examination algorithm is utilized to segment the image and the good effect has obtained. The 13 points characteristics extraction method is employed to obtain and save the digital characteristics in order to send them to the BP network to take the training. The recognition of the BP neural network for the Arial typeface may reach up to 90%, which has improved the recognition rate. This system can reduce the interference of the human factors for the meter reading, and it also can achieve the purpose of remote meter reading.
The system is the integration of the sampling, the wireless transmission and the image recognition. It realizes the function of the remote meter reading and has the practical value. In the last content in the dissertation, the work of dissertation was summarized, and the ameliorative direction was also proposed.
引文
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[39] Nicolas Garcia-Pedrajas and Domingo Ortiz-Boyer. A cooperative constructive method for neural networks for pattern recognition[J]. Pattern Recognition, 2007.1(1): 80~98.
[40] Demuth H d, Beale M. Neural network toolbox use’s guide Version 4[M]. The math works Inc, 2000.
[41] 从 爽. 面向 MATLAB 工具箱的神经网络理论与应用[M]. 中国科学技术大学出版社, 1998.8.
[42] M. Egmont-Petersen, D. de Ridder and H. Handels. Image processing with neural networks—a review[J]. Pattern Recognition, 2002.10(10):82~96.
[43] Zheng Danian, Zhao Yannan, Wang Jiaxin. An efficient method of license platelocation[J]. Pattern Recognition Letters, 2005,26(5):2431~2438.
[44] Javad Sadri, Ching Y. Suen and Tien D. Bui. A genetic framework using contextual knowledge for segmentation and recognition of handwritten numeral strings[J]. Pattern Recognition, 2007.3(3):78~82.
[45] Matthew Kyan, Ling Guan and Steven Liss. Integration of multiresolution image segmentation and neural networks for object depth recovery[J]. Pattern Recognition, 2005.7(7):35~42.
[46] 王田苗. 嵌入式系统设计与实例开发[M]. 北京:清华大学出版社, 2002.
[47] 陈予吒. 基于 uS/OS-II 的嵌入式系统设计与实现[D]. 浙江:浙江大学, 2004.
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[2] 毛永毅,张丽媚,岳慧芬. 基于短消息的自动抄表系统[J]. 电测与仪表, 2002,39(3):38~41.
[3] 粟玉霞,徐建政,刘爱兵. GPRS 技术在自动抄表中的应用[J]. 电力自动化设备, 2003,23(12):52~54.
[4] 许建华,李东伟,王 玮 . 自动抄表技术的应用和发展 [J]. 供用电 , 2004.2(1):1~31.
[5] 雷 勇. 仪表图像信息采集处理方法的研究[D]. 大连海事大学, 2004.3:20~26.
[6] 董 辉. 基于神经网络的图像识别系统的研究及实现[D]. 西南农业大学硕士论文, 2005.6:17~19.
[7] 朱 虹. 数字图像处理技术[M]. 科技出版社, 2005.10.
[8] 曹鸿霞. BP 神经网络在数字识别中的应用[J]. 湖北广播电视大学学报, 2006.11:174~176.
[9] 马 彪,杨建华,刘文琦. 基于 GPRS 的嵌入式数据采集系统[J]. 中国仪器仪表, 2005 年第 11 期.
[10] 邵小良. 嵌入式无线远程图像监视终端的设计与实现[D]. 重庆大学硕士学位论文, 2005.4:5~20.
[11] Juan Wei, ZhengGuang Liu, Zhi Chai, Jing Yuan, Jianyu Lian and Gary X Shen. A realization of digital wireless transmission for MRI signals based on 802.11b[J]. Journal of Magnetic Resonance, In Press, Accepted Manuscript, Available online 12 March 2007.
[12] 时道波. Premiere 中常用 12 种图像文件格式简介[J]. 中国有线电视, 2005(22):2232~2233.
[13] Kenneth R.Castleman,朱志刚等. 数字图像处理[M]. 电子工业出版社, 2002.10.
[14] 章毓晋. 图像工程(上册)-图像处理与分析[M]. 清华大学出版社, 1999.3.
[15] 吕凤頨. C++语言开发教程[M]. 清华大学出版社,1999.
[16] 高志伟. Visual C++程序设计教程与上机指导[M]. 北京大学出版社, 2006.
[17] 王俊杰,黄心汉. 一种对图像进行快速二值化处理的方法[J]. 电子应用技术, 1998.10:20~25.
[18] Ilya Blayvas, Alfred Bruckstein and Ron Kimmel. Efficient computation of adaptive threshold surfaces for image binarization[J]. Pattern Recognition, 2006.1(1): 89~101.
[19] Kan Chao, Srinath M D. Combined features of cubic B-spline wavelet moments and Zernike moments for invariant character recognition[J]. Information Technology, 2001.4 (2):511~515.
[20] 崔凤奎等. 二值图像细化算法的比较与改进[J]. 洛阳工学院学报,1997,18(4),48~52.
[21] Wang PSP,A Fast and Flexible Thinning Algorithm[J]. IEEE Trans Computer, 1989,38(5):741~745.
[22] Lam Louisa,Thinning Methodologies-A Comprehensive Survey,IEEE Trans[J]. On Pattern Analysis and Machine Intelligence, 1992,14(9):869~885.
[23] Tzu-Chao Lin. A new adaptive center weighted median filter for suppressing impulsive noise in images[J]. Information Sciences, 2007.2(4):1073~1087.
[24] David McG.Sauire. Invariance Signatures: Characterizing Contours by Their Departures from Invariance[J]. Computer Vision and Image Understanding, 2000.7(117):284~316.
[25] Dinggang Shen, Horace H d Ip. Discriminative wavelet shape descriptors for recognition[J]. Pattern Recognition, 1999,(32):151~165.
[26] Schubert, Li Hui. Chinese word segmentation and its effect on information retrieval[J]. Information Processing and Management, 2004.40(1):161~190.
[27] Kenneth R Castleman. 数字图像处理技术[M]. 电子工业出版社, 1998.
[28] 吴立德. 计算机视觉[M]. 复旦大学出版社, 1993.
[29] M.A.Arregui,J.A.Mitxelena. Automatic recognition of number plates[J]. The Ivth Spanish Symposium, 1992.1(1):239~258.
[30] 张重阳,娄 震. 基于轮廓和统计特征的手写体数字识别[J]. 计算机工程与应用, 2004,40(9):83~87.
[31] J.A.G Nijhuis. Car License Plate Recognition with Neural Networks and Fuzzy Logic[J]. IEEE, 1995,123~128.
[32] Yong Sung Soh. Design of Real Time Vehicle Identification System[J]. IEEE, 1994.4:235~240.
[33] 徐俊波,许庆国,周传光,赵 文. 反馈神经网络进展[J]. 化工自动化及仪表, 2003,30(1):6~10.
[34] 李驰新. 基于神经网络的交通事件检测系统研究[D]. 兰州理工大学硕士学位论文, 2006.5:10~42.
[35] Huang Y S. The combination of Multiple Classifiers by a Neural Network Approach[J]. International Journal Pattern Recognition and Artificial Intelligence, 1995.9(3):825~834.
[36] 飞思科技产品研发中心. MATLAB 6.5 辅助神经网络分析与设计[M]. 电子工业出版社, 2004.6:45~77.
[37] 杨淑莹. 图像模式识别—VC++技术实现[M]. 清华大学出版社,北京交通大学出版社, 2005.5:115~134.
[38] 李文容. 基于神经网络的离线数字识别技术的 DSP 实现[D]. 西安交通大学硕士论文, 2006.4:12~31.
[39] Nicolas Garcia-Pedrajas and Domingo Ortiz-Boyer. A cooperative constructive method for neural networks for pattern recognition[J]. Pattern Recognition, 2007.1(1): 80~98.
[40] Demuth H d, Beale M. Neural network toolbox use’s guide Version 4[M]. The math works Inc, 2000.
[41] 从 爽. 面向 MATLAB 工具箱的神经网络理论与应用[M]. 中国科学技术大学出版社, 1998.8.
[42] M. Egmont-Petersen, D. de Ridder and H. Handels. Image processing with neural networks—a review[J]. Pattern Recognition, 2002.10(10):82~96.
[43] Zheng Danian, Zhao Yannan, Wang Jiaxin. An efficient method of license platelocation[J]. Pattern Recognition Letters, 2005,26(5):2431~2438.
[44] Javad Sadri, Ching Y. Suen and Tien D. Bui. A genetic framework using contextual knowledge for segmentation and recognition of handwritten numeral strings[J]. Pattern Recognition, 2007.3(3):78~82.
[45] Matthew Kyan, Ling Guan and Steven Liss. Integration of multiresolution image segmentation and neural networks for object depth recovery[J]. Pattern Recognition, 2005.7(7):35~42.
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