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仓储安防监控系统的设计【含论文、开题报告、外文翻译】,含论文、开题报告、外文翻译,仓储,监控,系统,设计,论文,开题,报告,外文,翻译

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任务书一、毕业设计（论文）的内容物流仓库作为物质资源的存储和转运，在经济生产中发挥着重大的作用。每天在库区进行工作的人员、车辆流动相当频繁，其安全防范工作单靠保安人员的巡逻是不够的，须同时通过在仓库内的相关重要场所设置防盗防范系统，才能达到实现其仓库的综合管理和安全防范的综合目的。本课题要求设计一仓储安防监控系统，其主要功能包括防火监测和入侵检测等。二、毕业设计（论文）的要求与数据1、了解仓储安防监控系统的行业现状和发展前景；2、掌握火灾监测及入侵检测的常用方法及相关技术；3、当火灾发生或有入侵时能实时报警；4、完成相关硬件制作及软件调试。三、毕业设计（论文）应完成的工作1、完成二万字左右的毕业设计说明书（论文）；在毕业设计说明书（论文）中必须包括详细的300-500个单词的英文摘要；2、独立完成与课题相关，不少于四万字符的指定英文资料翻译（附英文原文）；3、参考文献要15篇上，且至少1篇为英文。4、完成相关硬件调试并模拟演示，图表资料齐全（兼附计算机制版软件绘出的相关硬件的原理图和制版图）。5、提供元件清单及相关注释。四、 应收集的资料及主要参考文献1 张正义,张永征,郑晓海,段敬恩.自动仓储系统及其应用J.物流技术与应用,2006(07).2 林建敏,李伙友.浅谈龙岩烟草行业监控系统设计J.中小企业管理与科技(上旬刊), 2009(11).3 王建,王汝琳,王学民,刘世民.煤矿瓦斯监测无线传感器网络系统设计与实现J. 煤炭工程,2006(10)4 Rao.Guthikonda V. Microprocessors and microcomputer system M.Van Nostrband Reinblod Company, 1982, (43).187-2105 N. Sriskanthan,P.Tan,A.Karande.Blutooth based home automation systemJ. Microprocessors and Microsystems,2002,(26).281-2896 唐建新.开放式智能化、网络化的数控技术J.长春理工大学学报(综合版),2005(02)7马笑.我国自动化立体仓库市场与技术发展分析J.物流技术(装备版),2011(18).8樊贵香,闫执中.自动化仓库的发展现状及展望J.机械管理开发,2010(01).9谢芸,吴俊才,李莉.红外遥控防盗报警器J.咸宁学院学报,2010, (06).10梁礼明,翁发禄,丁元春.基于生产调配功能的自动化立体仓库系统设计研究J.商品储运与养护, 2007(03)五、试验、测试、试制加工所需主要仪器设备及条件 1、计算机一台 2、MATLAB软件 3、数字万用表 4、单片机开发系统任务下达时间：2015年12月28日毕业设计开始与完成时间：2015年12月28日至 2016年05 月22日组织实施单位： 电气工程系教研室主任意见：签字： 2015年12月30日院领导小组意见：签字： 2015年12月31日开题报告1毕业设计的主要内容、重点和难点等研究内容物流仓库作为物质资源的存储和转运，在经济生产中发挥着重大的作用。每天在库区进行工作的人员、车辆流动相当频繁，其安全防范工作单靠保安人员的巡逻是不够的，须同时通过在仓库内的相关重要场所设置防盗防范系统，才能达到实现其仓库的综合管理和安全防范的综合目的。本课题要求设计一仓储安防监控系统，其主要功能包括防火监测和入侵检测等。研究内容主要有一下几个方面：（1）了解仓储安防监控系统的行业现状和发展前景；（2）掌握火灾监测及入侵检测的常用方法及相关技术；（3）当火灾发生或有入侵时能实时报警；（4）完成相关硬件制作及软件调试。研究重点及难点重点：（1）仓库安防监控基本系统的硬件设计，使其基本硬件电路能满足火灾监控及入侵检测、报警要求。（2）选择合适的传感器，保证系统对火灾和入侵检测的灵敏度和准确性。（3）正确的设计报警程序。难点：（1）正确设计传感器的的放大电路，使之能够符合单片机采集数据要求同时满足报警要求。（2）能使单片机程序能够精确的检测到火灾和入侵信号并及时报警。（3）消除外界干扰对外传感器的影响，使报警功能准确、可靠。2准备情况（查阅过的文献资料及调研情况、现有设备、实验条件等）研究状况及发展趋势（1）仓储安防监控系统研究概况1984年在美国，世界上第一栋智能建筑“城市广场（CityPlace）”出现以后，美国、加拿大、欧洲、澳大利亚和东南亚等经济较发达地区的国家都先后提出了各种智能家居方案。智能家居在美国、德国、新加坡、日本等过都有广泛应用。中国加入世界贸易组织之后，西方安防企业看到了中国13亿人口的巨大市场，带着技术理念上的优势开始进军中国。我国的安全技术防范工作是从1979年公安部在河北省石家庄市召开的全国刑事技术预防专业工作会议以后才逐步开展建立起来的，至今已经30多年。比欧美发达国家起步大约晚20年。改革开以前，受经济科学技术发展的制约，中国安防事业很长时间是以人防为主，安全技术防范还只处于概念阶段，技术防范产品几乎是空白。改革开放以后，安防作为一个行业在上海、北京、广州等经济发达城市和地区悄然兴起，以处在改革前沿的深圳为例，依托本地先进的电子科技优势和得天独厚的地理位置优势，逐渐发展成为了全国安防产业的重要基地。进入21世纪，中国安防产业的发展已基本成型，安全技术防范产品行业又有了进一步的发展，智能建筑、智能小区建设异军突起，以及高科技电子产品、全数字网络产品的大量涌现，都极大地促进了技防产品市场的蓬勃发展。中国正在成为世界上最庞大的安全防范产品市场已经是不争的事实，“世界工厂”的逐步形成是中国安防行业成为国名经济的增长点和新兴朝阳产业。安防产业日渐成为中国经济建设领域里一支十分重要的生力军。随着国民经济的发展和经济全球化进程的加快，中国安防产业迅速发展；随着科技不断进步，安防行业领域不断扩大。报警运营、中介、资讯等专业化服务开始起步；产品种类不断丰富，发展到了视频监控、出入口控制、入侵报警、防爆安检等十几个种类，数千个品种；闭路监控发展迅猛，年增长率达到30%左右；沿海地区发展较快，形成了以珠江三角洲、长江三角洲、京津地区为中心的三大安防产业集群。目前，安防系统已由第一代的模拟系统，经第二代数字化、第三代的网络化安防系统，发展到第四代智能化安防系统。而前三代安防系统在日趋严峻的反恐处突的今天已经很大程度上失去了安防系统的预防和积极干预的功能，而智能化的安防系统显然能够成为应对恐怖主义袭击和处理突发事件的有力辅助工具。因此第四代的智能化是数字化、网络化、高清化安防系统发展的必然趋势，是构建新型安全防范及保障系统建设的必由之路。智能化是平安城市建设的需要；智能化是现代信息社会发展的需要；智能化是一个国家科技实力的体现；智能化也是一个国家人民创新力的体现。目前网络视频监控系统急需智能化的内容分析与识别功能，并已成为网络视频监控的热点话题及发展方向。安全防范系统的结构模式经历了一个简单到复杂、分散到组合再到集成的发展变化过程。从早期单一分散的电子防盗报警器或者是由多个报警器组成防盗报警系统到后来的报警联网系统、报警监控系统，发展到防盗报警视频监控出入口控制等综合防范系统。近年来，在智能建筑和社区安全防范中，又形成了融防盗报警、视频监控、出入口控制、访客查询、保安巡更、汽车场（库）管理等系统综合监控与管理的系统结构模式。主要参考文献1 金光文.安防系统工程方案设计、安全防范系统概述M.西安电子科技大学出版社,20062 陈杰，黄鸿 .传感器与检测技术.高等教育出版社,20023 雷玉堂，安防&智能化、安防&智能化概论.北京：电子工业出版社4 周霞.安防系统工程.机械工业出版社.20045 张伟 .家庭智能防盗报警系统研究D.山东科技大学，20066 王芳 .智能化住宅防盗防火报警系统D.大连理工大学，20037 许军峰 .联网型智能小区防盗报警系统D.西南交通大学，20038 王宁 .智能监控防盗报警系统D.同济大学，2008，（03）9 陈宁 . 家庭安防系统的研究D.天津大学，2009，（09）10 胡瑞敏.多媒体信源编码技术与安防监控应急系统.湖北科学技术出版社.2008现有设备和实验条件个人计算机、51单片机开发板、示波器、直流稳压电源、数字万用表，开放实验室 3、实施方案、进度实施计划及预期提交的毕业设计资料实施方案具体方案如下：本系统包括一下几个部分：一、探测报警系统探测报警系统是用来探测入侵者的移动或其它行动（如防火、防盗、防爆、防劫等）的报警系统。当系统建立运行时，只要有入侵者的行为出现，就能发出报警信号。报警探测器是由传感器和信号处理组成的，用来探测入侵者入侵行为的，由电子和机械部件组成的装置，是防盗报警系统的关键，而传感器又是报警探测器的核心元件。采用不同原理的传感器件，可以构成不同种类、不同用途、达到不同探测目的的报警探测装置。（1）报警探测器按工作原理主要可分为红外报警探测器、微波报警探测器、被动式红外/微波报警探测器、玻璃破碎报警探测器、振动报警探测器、超声波报警探测器、激光报警探测器、磁控开关报警探测器、开关报警探测器、视频运动检测报警器、声音探测器等许多种类。（2）报警探测器按工作方式可分为主动式报警探测器和被动式报警探测器。（3）报警探测器按探测范围的不同又可分为点控报警探测器、线控报警探测器、面控报警探测器和空间防范报警探测器。 所以此次方案可以采用温度探测器（达到一定温度）和烟雾探测器（一定的烟雾浓度）。二、控制器控制器由信号处理器和报警装置组成。（1）信号处理器：用51单片机对传来的信号进行处理，判断有没有报警；（2）报警装置：声（喇叭）、光（红色灯炮）等。 进度实施计划第一阶段（2015-12-28至2016-3-6）：查阅资料,拟定方案。第二阶段（2016-3-6至2016-3-16）：方案的可行性分析。第三阶段（2016-3-16至2016-4-6）：设计硬件电路，器件选型，进行调试。第四阶段（2016-4-6至2016-5-10）：软件编程与硬件进行联调，最后提交作品。预期提交的毕业设计资料1、 两万字以上的毕业设计说明书（兼附15篇以上的参考文献）；在毕业设计说明书中应包括300-500个单词的英文摘要及关键词；2、 与课题相关英文资料的翻译（约4万英文字符，附英文全文）；3、 仓储安防监控系统的研究和实现方案；4、 系统的硬件和完成相应软件程序设计，给出硬件实现原理图和制版图；5、 软件清单及相关注释。指导教师意见指导教师（签字）： 2015年12月日开题小组意见开题小组组长（签字）：2016年1 月日 院（系、部）意见 主管院长（系、部主任）签字： 2016年1月日- 5 -The Ninth International Conference on Electronic Measurement & Instruments ICEMI 2009 A Near Infrared Imaging Detection System Based on Davinci Platform Li Hua, Zhang Shi-chao, Han Chao, Zheng Ming, Meng Xiao-feng Li Hua: Professor, Department of Physics, Beihang University, Beijing, China Email: lihua Zhang Shi-chao: Department of Physics, Beihang University, Beijing, China Email: zscbuaa126. Abstract Infrared imaging detection technology is a high-tech, involving of optics, electronics, machinery and computer science in many fields. The technology now plays a crucial role in detection of early warning, identification tracking, public security, fire alarm and applications in many other fields. This paper mainly expands on a near-infrared imaging detection system based on Davinci Digital Signal Processor (DSP) platform. And this article describes the schematic, hardware structure and image data processing algorithm of the near-infrared imaging detection system in detail. Firstly, infrared radiation from the tracked target is focused on the CMOS near infrared image sensor through an infrared optical lens. The image sensor controlled by a Complex Programmable Logic Device (CPLD) produces the corresponding image data, and then the image data is scaled by the CPLD to fit the following data processing. The CPLD also plays a key role in video buffering and noise reduction. The Davinci DSP-SOC will then accept the data flow afterward, and calculate the location of the target with the corresponding image processing algorithms to control the servos of horizontal and vertical, locking and tracking the infrared target in real time. The ARM9 core in the DSP-SOC is operated with Linux, which is free and open source. The user can humanly program the core DSP algorithms and tracking algorithms with a PC to get a much better effect of tracking. With a touch-screen and TFT LCD, the system is much more convenient and user-friendly. Due to the high sensitivity and resolution, the near infrared imaging detection system can be used at night or in very low visibility, having an excellent target recognition capability and precision- detection capability. Keywords Infrared image detection, Infrared image processing, CMOS, DSP-SOC, ARM9. I. INTRODUCTION Infrared detection technology is a high-tech using of infrared detectors to capture infrared radiation to search and track the target. It is featured by high precision, free from the effects of radio frequency interference and can be operated day and night. The technology is usually used in fire alarm and public safety. And it can be roughly divided into non-imaging and imaging detection technology. Non-imaging infrared detection technology makes use of one or several infrared detectors to capture and track the target, for which giving out infrared radiation due to thermal movement of the atoms and molecules. Because of the structure, it can be easily interfered by the normal work of clouds, fog and dust impact. Infrared imaging detection is usually making use of infrared detector array to detect the infrared radiation given out by the target. The detector array is usually a CCD or CMOS image sensor and the image quality is similar to television. But it can work at night and in very low visibility, which the television detection system can be difficult to work in. Infrared imaging detection technology has now become a major direction of the future use. There are many ways to achieve infrared imaging, mainly using the following two ways: (1) Multiple scanning infrared imaging detector linear arrays; (2) Multiple non-scanning infrared imaging detector planar arrays. The infrared imaging detector has been developed quickly since the 1970s, from linear array to the planar array, and from near-infrared to far infrared. The number of elements of infrared detector plane array is increasing. Infrared imaging detection system has a good sensitivity and high spatial resolution, a wide range of dynamic tracking as well as the effective range, compared to the non-imaging infrared detection. Infrared imaging detection system has better ability to complete target identification and precision detection. For an infrared imaging detection system, the application of image processing and target recognition is a key feature difference from non-imaging infrared detection system. The sensitivity of the detector array, the performance of image processing and target recognition algorithm, the performance of detection algorithm and the mechanical mobility all determine the performance of the detection system. This paper will deep into the search of a near infrared imaging detection system based on Davinci DSP platform, which is suitable for video and image processing due to its structure. The platform has an ARM9 core transplanted with Linux operating system and a C64x+ DSP core to complete imaging algorithms in real time. The paper will be structured as follows; In Section ? we will expose the framework of the system; In Section ? we will deep into the study of hardware design of the image processing and recognition; In Section ? we will describe our approach of software structure, especially the Linux operating system migration; In section ? we will deep into the study of the image processing and 4-154_978-1-4244-3864-8/09/$25.00 2009 IEEE The Ninth International Conference on Electronic Measurement & Instruments ICEMI 2009 recognition algorithms and methods suitable for the infrared imaging detection system. II. SYSTEM FRAMEWORK The system can be roughly divided into three parts, the hardware part, the software part and mechanical part. All parts above are an organic whole for the close relation mutually. And the block diagram of the system is showed in figure 1 below. Fig.1. Block diagram of the near infrared image detection system base on Davinci platform As shown in figure 1, the infrared radiation from the tracked target is focused on the CMOS near infrared image sensor through an infrared optical lens. With a CPLD the corresponding image data flow is scaled and buffered. The Davinci DSP-SOC will accept the data flow afterward, and then calculate the location of the target with the corresponding image processing algorithms. Since has got the location information of the target, the DSP then controls the servos of horizontal and vertical, locking and tracking the infrared target in real time. The ARM9 core in the DSP-SOC is operated with Linux, which is free and open source. The user can humanly program the core DSP algorithms and tracking algorithms with a PC. With a touch-screen and TFT LCD, the system is much more convenient and user-friendly. Due to the high sensitivity and resolution, the near infrared imaging detection system can be used at night and in very low visibility, having an excellent target recognition capability and precision- detection capability. III. THE HARDWARE DESIGN To capture the infrared image signal in very low visible circumstance, image sensor with high sensitivity at near infrared band shall be applied. The most popularly used sensors are CCD and CMOS image sensor. Considering the feature differences of the both in table 1, CMOS image sensor is chosen in the design for its high sensitivity around near infrared band, low cost and low power consumption. Table1. Feature differences between CCD and CMOS image sensor. ParameterSensitivity at infrared band Cost Noise Power ConsumptionCCD ModerateHigh Low High CMOS High Low High Low The key part of the hardware is the DSP-SOC, including an ARM core and a DSP core on the chip. The ARM core is used as the master to handle complex system work and DSP core as the slaver, handling video processing and recognition task. To give a much more clear view of the hardware structure, the block diagram in figure 2 will briefly and clearly presents the video processing subsystem (VPSS) on the chip. Fig.2. Block diagram of the video processing subsystem The video processing subsystem provides an input interface (video processing front end, VPFE) for external imaging peripherals such as image sensors, video decoders, etc.; an output interface (video processing back end, VPBE) for display devices, such as analog SDTV displays, digital LCD panels, HDTV video encoders, etc. In addition to these peripherals, there is a set of common buffer memory and DMA control to ensure efficient use of the DDR2 burst bandwidth. The shared buffer logic/memory is a unique block that is tailored for seamlessly integrating the VPSS into an image/video processing system. It is imperative that the VPSS utilize DDR2 bandwidth efficiently due to both its large bandwidth requirements and the real-time requirements of the VPSS modules. Because it is possible to configure the VPSS modules in such a way that DDR2 bandwidth is exceeded, a set of user-accessible registers is provided to monitor overflows or failures in data transfers. Therefore the DSP is suitable for the image detection task. As the DDR2 offers high-bandwidth for the system operation, the quality of the PCB design of DDR2 will affects the stability and robustness of the whole system. As the highest frequency on board is nearly Gigahertz, the most challenging work in the design is how to resolve Electro Magnetic Compatibility of the whole system. 4-155The Ninth International Conference on Electronic Measurement & Instruments ICEMI 2009 As required, the PCB should be at least applied with six layers to resolve EMC, and the minimum PCB stackup required is shown in Table 2. Each signal layer has a ground plane to refer to form microstrips and striplines. The multilayer design also benefits to the signal integrity and power integrity. Table2. Minimum PCB stackup required. The routing of DDR2 will be discussed in detail in the following paragraphs. The CK and ADDR_CTRL net class is completely sourced by the DSP to the DDR2 devices. Each net is a balanced T route, see Figure 3. The length of segment A should be maximized and the overall length from A to B or A to C should be minimized. Ideally, the PCB delay of the CK net class is identical to the delay for the ADDR_CTRL net class. All nets in the CK and ADDR_CTRL net classes are matched in length to each other within 100 mils. And the nets in the CK net class are laid out as a differential pair to achieve high reliability and noise immunity. Other traces should be kept away from the CK net class traces by at least 4w center-to-center spacing (recall that w = minimum trace width/space). Traces within the ADDR_CTRL net class should be spaced at least 3w center-to-center from each other. Traces of other net classes should be kept 4w away from the ADDR_CTRL net class. Fig.3. Topology Requirements for ADDR_CTRL and Clock Net Classes The eight net classes that make up the four DQSB s and four DQB bytes have the same routing rules. Note that the skew matching is required between the DQBn net class and its associated DQSBn net class. These net classes are sourced by the DSP device during writes and are sourced by the DDR2 devices during reads. Ideally, the PCB delay of the DQSBn net class is identical to the delay for the DQBn net class. All nets in the DQSBn and DQBn net class should be matched in length to each other within 100 mils. The longest trace permissible is equal to the longest Manhattan distance of the DQSBn and DQBn net classes. Other traces should be kept away from the DQSBn net class traces by at least 4w center-to-center spacing. Traces within the DQBn net classes should be spaced at least 3w center-to-center from each other. Traces of other net classes should be kept 4w away from the DQBn net class. To give a clear view of the hardware routing, the PCB of the DSP to DDR2 is shown in detail in figure 4 below. Fig.4. PCB of the DSP interfaces with DDR2 Before the PCB was machining, simulation was successfully carried out with 133 MHz clock rate and 533MHz data rate to ensure that the system would work properly, and the simulation wave of address bus and data bus is shown in figure 5 and 6 respectively. Fig.5. Simulation of DDR2 addresses bus and clock bus at 133MHz. Fig.6. Simulation of DDR2 data bus at 533 MHz 4-156The Ninth International Conference on Electronic Measurement & Instruments ICEMI 2009 IV. THE SOFTWARE DESIGN Linux is a Unix-like operating system. After decades of development, the Linux operating system becomes more and more mature and popular. Featured as open source, the Linux OS is free and flexible to use. Further more, Linux is suitable for workstation, server, personal computer and embedded system, for it supports multiple architectures, CISC and RISC, such as X86, ARM, MIPS, and so on. An embedded applications is usually transplanted with an operating system, and Linux is a most popular one around the world, for it is open source and with high reliability. We will make full exploit of the Linux OS and its transplantation in our design. First of all, the kernel is the heart of the Linux operating system, which offers management of process, management of memory, file system, device control and network. The function block is shown in figure 7. Fig.7. Function block of the kernel of Linux The process management module is a key block of the Linux kernel, taking charge of creating and terminating process. Communication through signal, pipeline or communication primitives among different process is a basic function for the whole system, which is accomplished by the kernel. The memory management module is to ensure all the processes can safely share the system memory. And what s more, it should also support virtual memory mode to highly improve the efficiency of memory usage. Offering a common interface for peripherals, the file system module is used to support storage and drivers for peripherals and the virtual file system hides details of different hardware. BootLoader is the code run immediately after power on. With the BootLoader, hardware devices can be initialized, and massages can be created and delivered to the kernel through the corresponding mechanisms. And then the BootLoader brings the system to a proper mode and load the kernel. Figure 8 shows the basic flow chart of the BootLoader. The BootLoader is closely depended on the hardware environment. And besides of architecture, the BootLoader also depends on the board configurations, especially for the embedded applications. Fig.8. Flow chart of the BootLoader After the BootLoader initialize the whole system and then release the authority to the Linux kernel, the kernel then works. The device driver is the routine working between the hardware and applications in the kernel space. Natured as a converter between logic devices and physical devices, its task is to initialize and manipulate the corresponding I/O devices. The device driver masks details of the hardware for the applications. With the device driver, the applications can control hardware device as easy as manipulate common files. The application software of the infrared image detection system is designed to accomplish the image processing and recognition tasks with the corresponding algorithms. The algorithms will be detailed in the following section. V. IMAGING PROCESSING AND RECOGNITION ALGORITHMS AND METHODS The image processing and recognition program is completed on the application layer of Linux. And the flow chart of the application is shown in figure 9. Besides other layers design, we will focus on the image processing and recognition algorithms and methods used in the application layer according to the flow chart. The infrared image is usually monochrome or pseudo-coloured and with low contrast, sometimes might be unclear or difficult to recognize. For example, it might be not easy to figure out the bird hidden in lush foliage in a picture taken at night or in very low visibility. Image enhancement is introduced to enlarge the details in gray scale or spatial to obtain a clear image. There are some methods used in our design, such as gray scale enhancement, contrast enhancement and histogram adjustment. Contrast enhancement magnifies n times the gray value of the entire pixels, that is; ?g,x ynf x y? 4-157The Ninth International Conference on Electronic Measurement & Instruments ICEMI 2009 The gray value in the original image is ?,f x y and the gray value in the contrast enhanced image is?,g x y, which is n times the gray value in the original image. Histogram adjustment is a method to compress the gray value with fewer pixels, and extend the gray value with more pixels. After contrast enhancement and histogram adjustment, details in the image can be exposed. And the corresponding histogram is flattered after the process. The original image often distributed with random noise, making the image deteriorated. In order to get rid of such noise, image smoothing or median filter are used. With the random noise of the original image being removed, sharp details are also lost during the process. Fig.9. Flow chart of the application After the image processing, image recognition is finally used to fulfill the infrared imaging detection task. Edge detection and extraction based on differential is applied. Due to the dramatic changes in gray value of the brink, it is clear that differential function can well extract the changes. Suppose ?, x y presents the location of a pixel, and ?,f x y presents the gray value of it. With a vector?,xyG x yff?, the edge can be easily figured out by the gradient. xf and yf are the differential along horizontal and vertical, calculated with the following equations. ?1,1,xyff xyf x yff x yf x y? ? So the intensity of the edge is22xyff?, and the direction is along the vector?,xyff. Anther method usually used is edge detection and extraction based on template matching. And template matching is responsible for researching the consistency of image and the corresponding template. Pattern recognition is applied to classify patterns from each other to det