WO2019205377A1 - 牲畜识别方法、装置及存储介质 - Google Patents

牲畜识别方法、装置及存储介质 Download PDF

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WO2019205377A1
WO2019205377A1 PCT/CN2018/102124 CN2018102124W WO2019205377A1 WO 2019205377 A1 WO2019205377 A1 WO 2019205377A1 CN 2018102124 W CN2018102124 W CN 2018102124W WO 2019205377 A1 WO2019205377 A1 WO 2019205377A1
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photo
animal
stitched
livestock
samples
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French (fr)
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刘奡智
王健宗
肖京
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands

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  • the present application relates to the field of image recognition technologies, and in particular, to a livestock identification method, apparatus, and storage medium.
  • animal husbandry is one of the important sources of human access to food.
  • livestock death is a frequent occurrence.
  • livestock death it will usually cause huge economic losses for these farmers. It may less inhibit the enthusiasm of potential farmers to join the animal husbandry, and cause potential obstacles to the development of animal husbandry; on the other hand, increase the probability that farmers will reduce the probability of livestock sickness through abnormal routes (for example, drug control) to provide livestock survival.
  • abnormal routes for example, drug control
  • the present application provides a livestock identification method, apparatus and storage medium, the main purpose of which is to remotely identify livestock, and reduce the recognition cost and improve the recognition efficiency.
  • the present application provides a livestock identification method, the method comprising:
  • Receiving step receiving a photo of each preset part of the animal to be identified and a corresponding identity to be verified;
  • a splicing step splicing photos of respective preset parts of the animal to be identified into a spliced photograph of the animal to be identified according to a predetermined splicing rule;
  • Determining step determining a livestock identification model corresponding to the identity identifier according to the mapping relationship between the identity identifier to be verified and the livestock identification model;
  • the recognizing step inputting the stitched photograph of the animal to be identified into the determined pre-trained livestock recognizing model, and outputting the recognition result.
  • the present application also provides an electronic device comprising a memory and a processor, the memory including a livestock identification program, the livestock identification program being executed by the processor to implement the following steps:
  • Receiving step receiving a photo of each preset part of the animal to be identified and a corresponding identity to be verified;
  • a splicing step splicing photos of respective preset parts of the animal to be identified into a spliced photograph of the animal to be identified according to a predetermined splicing rule;
  • Determining step determining a livestock identification model corresponding to the identity identifier according to the mapping relationship between the identity identifier to be verified and the livestock identification model;
  • the recognizing step inputting the stitched photograph of the animal to be identified into the determined pre-trained livestock recognizing model, and outputting the recognition result.
  • the present application also provides a computer readable storage medium including a livestock identification program that, when executed by the processor, implements livestock identification as described above Any step in the method.
  • the livestock identification method, the electronic device and the computer readable storage medium provided by the present application receive the photo of each preset part of the animal to be identified and the corresponding identity to be verified, and the livestock to be identified according to a predetermined splicing rule
  • the photos of the respective preset parts are stitched into the stitched photos of the animals to be identified, and the stitched photos are input into the pre-trained livestock recognition model, and the recognition result is output. Since the information transmission can be completed through the network without the need for field sampling, the remote batch identification of low cost and high efficiency for livestock can be realized by using the present application.
  • FIG. 1 is a schematic diagram of a preferred embodiment of an electronic device of the present application.
  • FIG. 2 is a block diagram showing the program of the livestock identification program of Figure 1;
  • FIG. 3 is a flow chart of a preferred embodiment of a livestock identification method of the present application.
  • the application provides an electronic device.
  • FIG. 1 it is a schematic diagram of a preferred embodiment of the electronic device 1 of the present application.
  • the electronic device 1 receives a photo of each preset part of the animal to be identified and a corresponding identity to be verified, obtains a stitched photo of the animal to be identified according to a predetermined stitching rule, and uses the pre-trained livestock identification.
  • the model generates a recognition result for the stitched photo.
  • the electronic device 1 may be a terminal device having a storage and computing function, such as a server, a smart phone, a tablet computer, a portable computer, a desktop computer, or the like.
  • the server when the electronic device 1 is a server, the server may be one or more of a rack server, a blade server, a tower server, or a rack server.
  • the electronic device 1 includes a memory 11, a processor 12, a network interface 13, and a communication bus 14.
  • the memory 11 includes at least one type of readable storage medium.
  • the at least one type of readable storage medium may be a non-volatile storage medium such as a flash memory, a hard disk, a multimedia card, a card type memory, or the like.
  • the readable storage medium may be an internal storage unit of the electronic device 1, such as a hard disk of the electronic device 1.
  • the readable storage medium may also be an external memory 11 of the electronic device 1, such as a plug-in hard disk equipped on the electronic device 1, a smart memory card (SMC). , Secure Digital (SD) card, Flash Card, etc.
  • SMC smart memory card
  • SD Secure Digital
  • the readable storage medium of the memory 11 is generally used to store an operating system, a livestock identification program 10, a livestock identification model, and photos of respective preset parts of various animals and corresponding identifications and the like.
  • the memory 11 can also be used to temporarily store data that has been output or is about to be output.
  • the processor 12 in some embodiments, may be a Central Processing Unit (CPU), microprocessor or other data processing chip for running program code or processing data stored in the memory 11, such as executing a livestock identification program. 10 and so on.
  • CPU Central Processing Unit
  • microprocessor or other data processing chip for running program code or processing data stored in the memory 11, such as executing a livestock identification program. 10 and so on.
  • the network interface 13 may include a standard wired interface, a wireless interface (such as a WI-FI interface). Typically used to establish a communication connection between the server 1 and other electronic devices or systems.
  • the communication bus 14 is used to implement connection communication between the above components.
  • FIG. 1 shows only the electronic device 1 having the components 11-14 and the livestock identification program 10, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead.
  • the electronic device 1 may further include a user interface
  • the user interface may include an input unit such as a keyboard, a voice input device such as a microphone, a device with voice recognition function, a voice output device such as an audio, a headphone, etc.
  • the user interface may also include a standard wired interface and a wireless interface.
  • the electronic device 1 may further include a display, which may also be referred to as a display screen or a display unit.
  • a display may also be referred to as a display screen or a display unit.
  • it may be an LED display, a liquid crystal display, a touch liquid crystal display, and an Organic Light-Emitting Diode (OLED) display.
  • the display is used to display information processed in the electronic device 1 and a user interface for displaying visualizations.
  • the electronic device 1 further comprises a touch sensor.
  • the area provided by the touch sensor for the user to perform a touch operation is referred to as a touch area.
  • the touch sensor described herein may be a resistive touch sensor, a capacitive touch sensor, or the like.
  • the touch sensor includes not only a contact type touch sensor but also a proximity type touch sensor or the like.
  • the touch sensor may be a single sensor or a plurality of sensors arranged, for example, in an array. The user can activate the livestock identification program 10 by touching the touch area.
  • the area of the display of the electronic device 1 may be the same as or different from the area of the touch sensor.
  • a display is stacked with the touch sensor to form a touch display. The device detects a user-triggered touch operation based on a touch screen display.
  • the electronic device 1 may further include radio frequency (RF) circuits, sensors, audio circuits, and the like, and details are not described herein.
  • RF radio frequency
  • Receiving step receiving a photo of each preset part of the animal to be identified and a corresponding identity to be verified;
  • a splicing step splicing photos of respective preset parts of the animal to be identified into a spliced photograph of the animal to be identified according to a predetermined splicing rule;
  • Determining step determining a livestock identification model corresponding to the identity identifier according to the mapping relationship between the identity identifier to be verified and the livestock identification model;
  • the recognizing step inputting the stitched photograph of the animal to be identified into the determined pre-trained livestock recognizing model, and outputting the recognition result.
  • the animal type corresponding to the identity to be verified should be the same as the type of the animal to be identified, otherwise the direct recognition fails, and the livestock identification program 10 is not required to be executed.
  • the predetermined location includes the face, ears, hooves, and tail of the animal.
  • the predetermined portion includes a pig's face, a pig's left ear or a pig's right ear, and any one of the four pig's trotters and the pig's tail.
  • the predetermined splicing rules include:
  • the face photo of the animal is at the upper left of the stitched photo
  • the ear photo of the animal is at the upper right of the stitched photo
  • the tail photo of the animal is at the bottom right of the stitched photo.
  • the splicing rule is a splicing rule with the highest recognition accuracy determined based on different preset parts.
  • the splicing step includes:
  • the photos of the preset parts of the animals to be identified are normalized as the photos of the first preset pixels whose background color is black, wherein the purpose of unifying the background colors to black is to eliminate the recognition accuracy of different background colors.
  • the stitched photo to be adjusted is reset to a stitched photo of the animal to be identified of the second preset pixel (eg, 227*227).
  • a livestock identification model is trained for each animal, the livestock identification model being a Convolutional Neural Networks (CNN) model, and the network structure of the livestock identification model is shown in Table 1.
  • CNN Convolutional Neural Networks
  • Table 1 Network structure of livestock identification model
  • the Layer Name column indicates the name of each layer
  • Input indicates the input layer
  • Conv indicates the convolution layer
  • Conv1 indicates the first convolution layer of the model
  • MaxPool indicates the maximum pooling layer
  • MaxPool1 indicates the first maximum of the model.
  • Fc represents the fully connected layer
  • Fc1 represents the first fully connected layer in the model
  • Softmax represents the Softmax classifier
  • Batch Size represents the number of input images of the current layer
  • Kernel Size represents the scale of the current layer convolution kernel (eg The Kernel Size can be equal to 3, indicating that the scale of the convolution kernel is 3*3)
  • Stride Size indicates the moving step size of the convolution kernel, that is, the distance moved to the next convolution position after completing one convolution
  • Pad Size indicates For the size of the image fill in the current network layer
  • N represents the number of animals that need to be identified. In the present embodiment, N is less than or equal to 4096.
  • the training process of the livestock identification model corresponding to the identity to be verified includes the following steps:
  • A1. Obtain a preset number of photo samples of each preset part of the animal, and assign a unique identity to each animal;
  • each photo sample combination includes photo samples of all the preset parts, and each photo sample combination includes photo samples of respective preset parts.
  • the number is 1, and it is assumed that, according to the above example, each photo sample combination includes a pig face photo sample, a pig ear photo sample, a pig's hoof photo sample, and a pigtail photo sample;
  • the spliced photo sample is divided into a first preset ratio (for example, 70%) of the training set and a second preset ratio (for example, 30%) of the verification set, and the spliced photo samples in the training set are used for the livestock.
  • the recognition model is trained, and after the training is completed, the accuracy of the livestock identification model is verified by using each spliced photo sample in the verification set. It can be understood that the sum of the first preset ratio and the second preset ratio is smaller than Or equal to 100%;
  • the training process ends. If the accuracy rate is less than or equal to the preset threshold, the number of the stitched photo samples is increased, and the steps are re-executed based on the added stitched photo samples. A4.
  • a preset threshold for example, 98.5%
  • the electronic device 1 of the above embodiment can realize the remote batch identification of the livestock by inputting the stitching photos of the various parts of the livestock to be identified into the livestock identification model of the head animal training and outputting the recognition result of the livestock.
  • the livestock identification program 10 can be partitioned into a plurality of modules that are stored in the memory 12 and executed by the processor 13 to complete the application.
  • a module as referred to in this application refers to a series of computer program instructions that are capable of performing a particular function.
  • the livestock identification program 10 can be divided into: a receiving module 110, a splicing module 120, a determining module 130, and an identifying module 140, and the functions or operating steps implemented by the modules 110-140 are the same as above. Similarly, it will not be described in detail here, by way of example, for example:
  • the receiving module 110 is configured to receive a photo of each preset part of the animal to be identified and a corresponding identity to be verified;
  • the splicing module 120 is configured to splicing photos of the preset parts of the animal to be identified into a spliced photo of the animal to be identified according to a predetermined splicing rule;
  • the determining module 130 is configured to determine, according to the mapping relationship between the identity identifier to be verified and the livestock identification model, a livestock identification model corresponding to the identity identifier;
  • the identification module 140 inputs the stitched photo of the animal to be identified into the determined pre-trained livestock recognition model, and outputs the recognition result.
  • the present application also provides a livestock identification method.
  • a flow chart of a preferred embodiment of the livestock identification method of the present application The following steps are implemented when the processor 12 of the electronic device 1 executes the livestock identification program 10 stored in the memory to implement the livestock identification method:
  • step S10 the receiving module 110 receives a photo of each preset part of the animal to be identified and a corresponding identity to be verified.
  • each animal is assigned a unique identity
  • the function of the livestock identification program 10 is to identify the stitched photo of the animal to be identified, and verify whether the received identity is the identity of the animal corresponding to the stitched photo. logo.
  • the splicing module 120 splices the photos of the preset parts of the animals to be identified into the spliced photos of the animals to be identified according to a predetermined splicing rule.
  • the preset portion includes a face, an ear, a hoof, and a tail of the animal.
  • the predetermined splicing rules include:
  • the face photo of the animal is at the upper left of the stitched photo
  • the ear photo of the animal is at the upper right of the stitched photo
  • the tail photo of the animal is at the bottom right of the stitched photo.
  • step S30 the determining module 130 determines the livestock identification model corresponding to the identity identifier according to the mapping relationship between the identity identifier to be verified and the livestock identification model.
  • a livestock identification model is trained for each animal, that is, the identity identifier has a one-to-one correspondence with the livestock identification model, and the determining module 130 determines the livestock corresponding to the identity to be verified according to the mapping relationship of the one-to-one correspondence. Identify the model.
  • the livestock identification model For the training process of the livestock identification model, please refer to the above detailed description about the electronic device 1, and no further details are provided herein.
  • step S40 the identification module 140 inputs the stitched photo of the animal to be identified into the determined pre-trained livestock recognition model, and outputs the recognition result.
  • the identification result includes the identification pass and the identification failure. If the recognition fails, the photo and the identity of the received preset parts do not match. If the identification is passed, the received identity is the identity of the animal to be identified. The photos and identifications of the received preset parts correspond to the same animal.
  • the livestock identification method by receiving a photo of each preset part of the animal to be identified and a corresponding identity to be verified, splicing the photos of the preset parts of the animal to be identified according to a predetermined splicing rule
  • the spliced photo of the animal to be identified is input into the animal identification model corresponding to the identity to be verified, and the recognition result of the recognition pass or recognition failure is output. Since the information transmission can be completed through the network without the need for field sampling, the livestock identification method can realize the low-cost and high-efficiency remote batch identification of the livestock, and the spliced photo is obtained by splicing photos of the respective preset parts.
  • the identification method can also improve the accuracy of livestock identification.
  • the embodiment of the present application further provides a computer readable storage medium, which may be a hard disk, a multimedia card, an SD card, a flash memory card, an SMC, a read only memory (ROM), and an erasable programmable Any combination or combination of any one or more of read only memory (EPROM), portable compact disk read only memory (CD-ROM), USB memory, and the like.
  • the computer readable storage medium stores therein a livestock identification program 10 that, when executed by the processor 13, performs the following operations:
  • Receiving step receiving a photo of each preset part of the animal to be identified and a corresponding identity to be verified;
  • a splicing step splicing photos of respective preset parts of the animal to be identified into a spliced photograph of the animal to be identified according to a predetermined splicing rule;
  • Determining step determining a livestock identification model corresponding to the identity identifier according to the mapping relationship between the identity identifier to be verified and the livestock identification model;
  • the recognizing step inputting the stitched photograph of the animal to be identified into the determined pre-trained livestock recognizing model, and outputting the recognition result.
  • the specific embodiment of the computer readable storage medium of the present application is substantially the same as the specific embodiment of the livestock identification method and the electronic device 1 described above, and details are not described herein again.

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Abstract

本申请提供了一种牲畜识别方法、装置及计算机可读存储介质，该方法包括以下步骤：接收待识别牲畜的各个预设部位的照片和相应的待验证的身份标识；按照预先确定的拼接规则将该待识别牲畜的各个预设部位的照片拼接成该待识别牲畜的拼接照片；根据所述待验证的身份标识与牲畜识别模型的映射关系，确定该身份标识对应的牲畜识别模型；将所述待识别牲畜的拼接照片输入确定的预先训练得到的牲畜识别模型，输出识别结果。本申请通过接收的身份标识确定对应的牲畜识别模型，利用确定的牲畜识别模型对待识别牲畜的各个预设部位的拼接照片进行识别，可以实现对牲畜的远程批量识别。

Description

牲畜识别方法、装置及存储介质
优先权申明
本申请要求于2018年4月26日提交中国专利局、申请号为2018103879875，发明名称为“牲畜识别方法、装置及存储介质”的中国专利申请的优先权，其内容全部通过引用结合在本申请中。
技术领域
本申请涉及图像识别技术领域，尤其涉及一种牲畜识别方法、装置及存储介质。
背景技术
目前，畜牧业是人类获取食物的重要来源之一。在牲畜养殖过程中，牲畜生病死亡是一个经常发生的事件，对大多数养殖户而言，若发生牲畜生病死亡事件，则通常会造成这些养殖户巨大的经济损失，这种风险一方面或多或少抑制了潜在养殖户投身畜牧业的积极性，给畜牧业的发展造成潜在的阻碍；另一方面增加了养殖户通过非正常途径(例如，药物控制)降低牲畜生病的概率从而提供牲畜的存活率的可能性，从而该食品安全构成极大的现实威胁。
为了最大程度降低这种风险带来的影响，很多保险公司推出了牲畜险，以保险的方式为养殖户规避这种风险。为了配合牲畜险的开展，目前出现了许多识别被保牲畜身份的现有识别方案，例如，为被投保的猪植入芯片、DNA识别、打耳标等方式对被保的猪进行身份识别，但这类现有识别方案成本较高、效率低下、无法远程批量识别。
发明内容
鉴于以上原因，本申请提供一种牲畜识别方法、装置及存储介质，其主要目的在于对牲畜进行远程批量识别，并降低识别成本，提高识别效率。
为实现上述目的，本申请提供一种牲畜识别方法，该方法包括：
接收步骤：接收待识别牲畜的各个预设部位的照片和相应的待验证的身 份标识；
拼接步骤：按照预先确定的拼接规则将该待识别牲畜的各个预设部位的照片拼接成该待识别牲畜的拼接照片；
确定步骤：根据所述待验证的身份标识与牲畜识别模型的映射关系，确定该身份标识对应的牲畜识别模型；及
识别步骤：将所述待识别牲畜的拼接照片输入确定的预先训练得到的牲畜识别模型，输出识别结果。
本申请还提供一种电子装置，该电子装置包括存储器和处理器，所述存储器中包括牲畜识别程序，该牲畜识别程序被所述处理器执行时实现如下步骤：
接收步骤：接收待识别牲畜的各个预设部位的照片和相应的待验证的身份标识；
拼接步骤：按照预先确定的拼接规则将该待识别牲畜的各个预设部位的照片拼接成该待识别牲畜的拼接照片；
确定步骤：根据所述待验证的身份标识与牲畜识别模型的映射关系，确定该身份标识对应的牲畜识别模型；及
识别步骤：将所述待识别牲畜的拼接照片输入确定的预先训练得到的牲畜识别模型，输出识别结果。
此外，为实现上述目的，本申请还提供一种计算机可读存储介质，所述计算机可读存储介质中包括牲畜识别程序，该牲畜识别程序被所述处理器执行时实现如上所述的牲畜识别方法中的任意步骤。
本申请提出的牲畜识别方法、电子装置及计算机可读存储介质，通过接收待识别牲畜的各个预设部位的照片和相应的待验证的身份标识，根据预先确定的拼接规则将该待识别牲畜的各个预设部位的照片拼接成该待识别牲畜的拼接照片，将该拼接照片输入预先训练好的牲畜识别模型，输出识别结果。因为无须实地采样，通过网络即可完成信息传递，所以利用本申请可以实现对牲畜低成本、高效率的远程批量识别。
附图说明
图1为本申请电子装置较佳实施例的示意图；
图2为图1中牲畜识别程序的程序模块图；
图3为本申请牲畜识别方法较佳实施例的流程图。
本申请目的的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
具体实施方式
为了使本申请的目的、技术方案和优点更加清楚明白，下面将结合若干附图及实施例，对本申请进行进一步详细说明。应当理解的是，此处所描述的具体实施例仅仅用以解释本申请，并不用于限定本申请。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例，都属于本申请保护的范围。
本申请提供一种电子装置。参照图1所示，为本申请电子装置1较佳实施例的示意图。在该实施例中，电子装置1接收待识别牲畜的各个预设部位的照片和相应的待验证的身份标识，按照预先确定的拼接规则得到待识别牲畜的拼接照片，利用预先训练好的牲畜识别模型生成对该拼接照片的识别结果。
所述电子装置1可以是服务器、智能手机、平板电脑、便携计算机、桌上型计算机等具有存储和运算功能的终端设备。在一个实施例中，当电子装置1为服务器时，该服务器可以是机架式服务器、刀片式服务器、塔式服务器或机柜式服务器等的一种或几种。
所述电子装置1包括存储器11、处理器12、网络接口13及通信总线14。
其中，存储器11包括至少一种类型的可读存储介质。所述至少一种类型的可读存储介质可为如闪存、硬盘、多媒体卡、卡型存储器等的非易失性存储介质。在一些实施例中，所述可读存储介质可以是所述电子装置1的内部存储单元，例如该电子装置1的硬盘。在另一些实施例中，所述可读存储介质也可以是所述电子装置1的外部存储器11，例如所述电子装置1上配备的插接式硬盘，智能存储卡(Smart Media Card，SMC)，安全数字(Secure Digital，SD)卡，闪存卡(Flash Card)等。
在本实施例中，所述存储器11的可读存储介质通常用于存储操作系统、 牲畜识别程序10、牲畜识别模型及各种牲畜的各个预设部位的照片和对应的身份标识等。所述存储器11还可以用于暂时地存储已经输出或者将要输出的数据。
处理器12在一些实施例中可以是一中央处理器(Central Processing Unit，CPU)，微处理器或其他数据处理芯片，用于运行存储器11中存储的程序代码或处理数据，例如执行牲畜识别程序10等。
网络接口13可以包括标准的有线接口、无线接口(如WI-FI接口)。通常用于在该服务器1与其他电子设备或系统之间建立通信连接。
通信总线14用于实现上述组件之间的连接通信。
图1仅示出了具有组件11-14以及牲畜识别程序10的电子装置1，但是应理解的是，并不要求实施所有示出的组件，可以替代的实施更多或者更少的组件。
可选地，该电子装置1还可以包括用户接口，用户接口可以包括输入单元比如键盘(Keyboard)、语音输入装置比如麦克风(Microphone)等具有语音识别功能的设备、语音输出装置比如音响、耳机等。可选地，用户接口还可以包括标准的有线接口、无线接口。
可选地，该电子装置1还可以包括显示器，也可以称为显示屏或显示单元。在一些实施例中可以是LED显示器、液晶显示器、触控式液晶显示器以及有机发光二极管(Organic Light-Emitting Diode，OLED)显示器等。显示器用于显示在电子装置1中处理的信息以及用于显示可视化的用户界面。
可选地，该电子装置1还包括触摸传感器。所述触摸传感器所提供的供用户进行触摸操作的区域称为触控区域。此外，这里所述的触摸传感器可以为电阻式触摸传感器、电容式触摸传感器等。而且，所述触摸传感器不仅包括接触式的触摸传感器，也可包括接近式的触摸传感器等。此外，所述触摸传感器可以为单个传感器，也可以为例如阵列布置的多个传感器。用户可以通过触摸所述触控区域启动牲畜识别程序10。
此外，该电子装置1的显示器的面积可以与所述触摸传感器的面积相同，也可以不同。可选地，将显示器与所述触摸传感器层叠设置，以形成触摸显示屏。该装置基于触摸显示屏侦测用户触发的触控操作。
该电子装置1还可以包括射频(Radio Frequency，RF)电路、传感器和 音频电路等等，在此不再赘述。
在上述实施例中，处理器12执行存储器11中存储的牲畜识别程序10时实现如下步骤：
接收步骤：接收待识别牲畜的各个预设部位的照片和相应的待验证的身份标识；
拼接步骤：按照预先确定的拼接规则将该待识别牲畜的各个预设部位的照片拼接成该待识别牲畜的拼接照片；
确定步骤：根据所述待验证的身份标识与牲畜识别模型的映射关系，确定该身份标识对应的牲畜识别模型；及
识别步骤：将所述待识别牲畜的拼接照片输入确定的预先训练得到的牲畜识别模型，输出识别结果。
可以理解的是，所述待验证的身份标识对应的牲畜种类应该与待识别牲畜的种类相同，否则直接识别失败，无须执行牲畜识别程序10。
在一个实施例中，所述预设部位包括牲畜的脸部、耳部、蹄部和尾部。例如，假设待识别牲畜为家猪，则所述预设部位包括猪脸、猪左耳或猪右耳、四只猪蹄中的任意一只猪蹄和猪尾。所述预先确定的拼接规则包括：
牲畜的脸部照片在所述拼接照片的左上方；
牲畜的耳部照片在所述拼接照片的右上方；
牲畜的蹄部照片在所述拼接照片的左下方；
牲畜的尾部照片在所述拼接照片的右下方。
需要解释的是，该拼接规则是基于不同预设部位所确定出来的识别精度最高的一个拼接规则。
在该实施例中，所述拼接步骤包括：
分别将所述待识别牲畜的各个预设部位的照片归一化为第一预设像素的背景色为黑色的照片，其中，将背景色统一为黑色的目的是为了消除不同背景色给识别精度造成的影响；
将该待识别牲畜的归一化后的各个预设部位的照片按照预先确定的拼接规则拼接成该待识别牲畜的待调整拼接照片；
将该待调整拼接照片重置为第二预设像素(例如，227*227)的该待识别牲畜的拼接照片。
可以理解的是，将待调整拼接照片的像素重置，可以方便后续的特征提取和减少特征维数。
在该实施例中，为每一头牲畜训练一个牲畜识别模型，所述牲畜识别模型为卷积神经网络(Convolutional Neural Networks，CNN)模型，该牲畜识别模型的网络结构如表1所示。
表1：牲畜识别模型的网络结构
Layer Name Output Size Kernel Size Stride Size Pad Size
Input 224 N/A N/A N/A
Conv1 64 3x3x3 1x1 1
Conv2 64 3x3x3 1x1 1
MaxPool1 64 N/A 2x2 0
Conv3 128 3x3x3 1x1 1
Conv4 128 3x3x3 1x1 1
MaxPool2 128 N/A 2x2 0
Conv5 256 3x3x3 1x1 1
Conv6 256 3x3x3 1x1 1
Conv7 256 3x3x3 1x1 1
MaxPool3 256 N/A 2x2 0
Conv8 512 3x3x3 1x1 1
Conv9 512 3x3x3 1x1 1
Conv10 512 3x3x3 1x1 1
MaxPool4 512 N/A 2x2 0
Conv11 512 3x3x3 1x1 1
Conv12 512 3x3x3 1x1 1
Conv13 512 3x3x3 1x1 1
MaxPool5 512 N/A 2x2 0
Fc1 4096 N/A 1x1 0
Fc2 4096 N/A 1x1 0
Fc3 N N/A N/A N/A
Softmax 2 N/A N/A N/A
其中，Layer Name列表示每一层的名称，Input表示输入层，Conv表示卷积层，Conv1表示模型的第1个卷积层，MaxPool表示最大值池化层，MaxPool1表示模型的第1个最大值池化层，Fc表示全连接层，Fc1表示模型中第1个全连接层，Softmax表示Softmax分类器，Batch Size表示当前层的输入图像数目，Kernel Size表示当前层卷积核的尺度(例如，Kernel Size可以等于3，表示卷积核的尺度为3*3)，Stride Size表示卷积核的移动步长，即做完一次卷积之后移动到下一个卷积位置的距离，Pad Size表示对当前网络层之 中的图像填充的大小，N表示需要识别的牲畜的数量，在本实施例中，N小于等于4096。
其中，所述待验证的身份标识对应的牲畜识别模型的训练过程包括如下步骤：
A1、获取预设数量的该种牲畜的各个预设部位的照片样本，为每头牲畜分配一个唯一的身份标识；
A2、将所述照片样本随机组合，生成多个照片样本组合，其中，每个照片样本组合包括所有所述预设部位的照片样本，且每个照片样本组合包括的各个预设部位的照片样本的数量为1，假设依上述例子，则每个照片样本组合包括一张猪脸照片样本、一张猪耳照片样本、一张猪蹄照片样本和一张猪尾照片样本；
A3、将各个照片样本组合中的照片样本按照预先确定的拼接规则拼接成待模型训练的拼接照片样本，以各个照片样本均属于所述待验证的身份标识对应牲畜的拼接照片样本作为正样本，以其他拼接照片样本作为负样本；
A4、将所述拼接照片样本分为第一预设比例(例如70％)的训练集和第二预设比例(例如30％)的验证集，利用训练集中的各个拼接照片样本对所述牲畜识别模型进行训练，并在训练完成后利用验证集中的各个拼接照片样本对该牲畜识别模型的准确率进行验证，可以理解的是，所述第一预设比例和第二预设比例之和小于或等于100％；
A5、若准确率大于预设阈值(例如98.5％)，则训练过程结束，若准确率小于或等于预设阈值，则增加所述拼接照片样本的数量，并基于增加的拼接照片样本重新执行步骤A4。
上述实施例提出的电子装置1，通过将待识别牲畜的各个部位的拼接照片输入为该头牲畜训练的牲畜识别模型，输出对牲畜的识别结果，可以实现对牲畜的远程批量识别。
在其他实施例中，牲畜识别程序10可以被分割为多个模块，该多个模块被存储于存储器12中，并由处理器13执行，以完成本申请。本申请所称的模块是指能够完成特定功能的一系列计算机程序指令段。
参照图2所示，为图1中牲畜识别程序10较佳实施例的程序模块图。在本实施例中，所述牲畜识别程序10可以被分割为：接收模块110、拼接模块 120、确定模块130及识别模块140，所述模块110-140所实现的功能或操作步骤均与上文类似，在此不再详述，实例性地，例如其中：
接收模块110，用于接收待识别牲畜的各个预设部位的照片和相应的待验证的身份标识；
拼接模块120，用于按照预先确定的拼接规则将该待识别牲畜的各个预设部位的照片拼接成该待识别牲畜的拼接照片；
确定模块130，用于根据所述待验证的身份标识与牲畜识别模型的映射关系，确定该身份标识对应的牲畜识别模型；
识别模块140，将所述待识别牲畜的拼接照片输入确定的预先训练得到的牲畜识别模型，输出识别结果。
此外，本申请还提供一种牲畜识别方法。参照图3所示，为本申请牲畜识别方法的较佳实施例的流程图。电子装置1的处理器12执行存储器中存储的牲畜识别程序10时实现牲畜识别方法的如下步骤：
步骤S10，接收模块110接收待识别牲畜的各个预设部位的照片和相应的待验证的身份标识。需要说明的是，本实施例中为每头牲畜分配有一个唯一的身份标识，牲畜识别程序10的作用是识别待识别牲畜的拼接照片，验证接收的身份标识是否为该拼接照片对应牲畜的身份标识。
步骤S20，拼接模块120按照预先确定的拼接规则将该待识别牲畜的各个预设部位的照片拼接成该待识别牲畜的拼接照片。在本实施例中，所述预设部位包括牲畜的脸部、耳部、蹄部和尾部。所述预先确定的拼接规则包括：
牲畜的脸部照片在所述拼接照片的左上方；
牲畜的耳部照片在所述拼接照片的右上方；
牲畜的蹄部照片在所述拼接照片的左下方；
牲畜的尾部照片在所述拼接照片的右下方。
步骤S30，确定模块130根据所述待验证的身份标识与牲畜识别模型的映射关系，确定该身份标识对应的牲畜识别模型。本申请实施例为每头牲畜训练一个牲畜识别模型，即身份标识与牲畜识别模型存在一一对应的关系，确定模块130根据该一一对应的映射关系，确定所述待验证身份标识对应的牲畜识别模型。所述牲畜识别模型的训练过程请参照上述关于电子装置1的详细介绍，在此不做赘述。
步骤S40，识别模块140将所述待识别牲畜的拼接照片输入确定的预先训练得到的牲畜识别模型，输出识别结果。所述识别结果包括识别通过和识别失败，若识别失败，说明接收的各个预设部位的照片和身份标识不匹配，若识别通过，则说明接收的身份标识为所述待识别牲畜的身份标识，接收的各个预设部位的照片和身份标识对应于同一头牲畜。
本实施例提出的牲畜识别方法，通过接收待识别牲畜的各个预设部位的照片和相应的待验证的身份标识，按照预先确定的拼接规则将该待识别牲畜的各个预设部位的照片拼接成该待识别牲畜的拼接照片，将该拼接照片输入待验证的身份标识对应的牲畜识别模型，输出识别通过或识别失败的识别结果。因为无须实地采样，通过网络即可完成信息传递，所以利用该牲畜识别方法可以实现对牲畜低成本、高效率的远程批量识别，由于所述拼接照片由各个预设部位的照片拼接得到，该牲畜识别方法还可以提高牲畜识别的精度。
此外，本申请实施例还提出一种计算机可读存储介质，所述计算机可读存储介质可以是硬盘、多媒体卡、SD卡、闪存卡、SMC、只读存储器(ROM)、可擦除可编程只读存储器(EPROM)、便携式紧致盘只读存储器(CD-ROM)、USB存储器等等中的任意一种或者几种的任意组合。所述计算机可读存储介质中存储有牲畜识别程序10，该牲畜识别程序10被所述处理器13执行时实现如下操作：
接收步骤：接收待识别牲畜的各个预设部位的照片和相应的待验证的身份标识；
拼接步骤：按照预先确定的拼接规则将该待识别牲畜的各个预设部位的照片拼接成该待识别牲畜的拼接照片；
确定步骤：根据所述待验证的身份标识与牲畜识别模型的映射关系，确定该身份标识对应的牲畜识别模型；及
识别步骤：将所述待识别牲畜的拼接照片输入确定的预先训练得到的牲畜识别模型，输出识别结果。
本申请之计算机可读存储介质的具体实施方式与上述牲畜识别方法和电子装置1的具体实施方式大致相同，在此不再赘述。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、装置、物品 或者方法不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、装置、物品或者方法所固有的要素。另外，各个实施例之间的技术方案可以相互结合，但是必须是以本领域普通技术人员能够实现为基础，当技术方案的结合出现相互矛盾或无法实现时应当认为这种技术方案的结合不存在，也不在本申请要求的保护范围之内。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到上述实施例方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本申请的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品存储在如上所述的一个存储介质中，包括若干指令用以使得服务器执行本申请各个实施例所述的方法。
以上仅为本申请的优选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利保护范围内。

Claims (20)

  1. 一种牲畜识别方法，应用于电子装置，其特征在于，该方法包括：
    接收步骤：接收待识别牲畜的各个预设部位的照片和相应的待验证的身份标识；
    拼接步骤：按照预先确定的拼接规则将该待识别牲畜的各个预设部位的照片拼接成该待识别牲畜的拼接照片；
    确定步骤：根据所述待验证的身份标识与牲畜识别模型的映射关系，确定该身份标识对应的牲畜识别模型；及
    识别步骤：将所述待识别牲畜的拼接照片输入确定的预先训练得到的牲畜识别模型，输出识别结果。
  2. 如权利要求1所述的牲畜识别方法，其特征在于，所述预设部位包括牲畜的脸部、耳部、蹄部和尾部。
  3. 如权利要求1所述的牲畜识别方法，其特征在于，所述预先确定的拼接规则包括：
    牲畜的脸部照片在所述拼接照片的左上方；
    牲畜的耳部照片在所述拼接照片的右上方；
    牲畜的蹄部照片在所述拼接照片的左下方；
    牲畜的尾部照片在所述拼接照片的右下方。
  4. 如权利要求2所述的牲畜识别方法，其特征在于，所述预先确定的拼接规则包括：
    牲畜的脸部照片在所述拼接照片的左上方；
    牲畜的耳部照片在所述拼接照片的右上方；
    牲畜的蹄部照片在所述拼接照片的左下方；
    牲畜的尾部照片在所述拼接照片的右下方。
  5. 如权利要求1所述的牲畜识别方法，其特征在于，所述拼接步骤包括：
    分别将所述待识别牲畜的各个预设部位的照片归一化为第一预设像素的背景色为黑色的照片；
    将该待识别牲畜的归一化后的各个预设部位的照片按照预先确定的拼接规则拼接成该待识别牲畜的待调整拼接照片；
    将该待调整拼接照片重置为第二预设像素的该待识别牲畜的拼接照片。
  6. 如权利要求1所述的牲畜识别方法，其特征在于，所述待验证的身份 标识对应的牲畜识别模型的训练过程包括如下步骤：
    A1、获取第一预设数量的某种牲畜的各个预设部位的照片样本，为每头牲畜分配一个唯一的身份标识，其中，该种牲畜与所述待识别牲畜的种类相同，分配的身份标识中包括所述待验证的身份标识；
    A2、将所述照片样本随机组合，生成多个照片样本组合，其中，每个照片样本组合包括所有所述预设部位的照片样本，且每个照片样本组合包括的各个预设部位的照片样本的数量为1；
    A3、将各个照片样本组合中的照片样本按照预先确定的拼接规则拼接成待模型训练的拼接照片样本，以各个照片样本均属于所述待验证的身份标识对应牲畜的拼接照片样本作为正样本，以其他拼接照片样本作为负样本；
    A4、将所述拼接照片样本分为第一预设比例的训练集和第二预设比例的验证集，利用训练集中的各个拼接照片样本对所述牲畜识别模型进行训练，并在训练完成后利用验证集中的各个拼接照片样本对该牲畜识别模型的准确率进行验证；
    A5、若准确率大于预设阈值，则训练过程结束，若准确率小于或等于预设阈值，则增加所述拼接照片样本的数量，并基于增加的拼接照片样本重新执行步骤A4。
  7. 如权利要求1所述的牲畜识别方法，其特征在于，所述牲畜识别模型为卷积神经网络模型。
  8. 一种电子装置，包括存储器和处理器，其特征在于，所述存储器中包括牲畜识别程序，该牲畜识别程序被所述处理器执行时实现如下步骤：
    接收步骤：接收待识别牲畜的各个预设部位的照片和相应的待验证的身份标识；
    拼接步骤：按照预先确定的拼接规则将该待识别牲畜的各个预设部位的照片拼接成该待识别牲畜的拼接照片；
    确定步骤：根据所述待验证的身份标识与牲畜识别模型的映射关系，确定该身份标识对应的牲畜识别模型；及
    识别步骤：将所述待识别牲畜的拼接照片输入确定的预先训练得到的牲畜识别模型，输出识别结果。
  9. 如权利要求8所述的电子装置，其特征在于，所述预设部位包括牲畜 的脸部、耳部、蹄部和尾部。
  10. 如权利要求8所述的电子装置，其特征在于，所述预先确定的拼接规则包括：
    牲畜的脸部照片在所述拼接照片的左上方；
    牲畜的耳部照片在所述拼接照片的右上方；
    牲畜的蹄部照片在所述拼接照片的左下方；
    牲畜的尾部照片在所述拼接照片的右下方。
  11. 如权利要求9所述的电子装置，其特征在于，所述预先确定的拼接规则包括：
    牲畜的脸部照片在所述拼接照片的左上方；
    牲畜的耳部照片在所述拼接照片的右上方；
    牲畜的蹄部照片在所述拼接照片的左下方；
    牲畜的尾部照片在所述拼接照片的右下方。
  12. 如权利要求8所述的电子装置，其特征在于，所述拼接步骤包括：
    分别将所述待识别牲畜的各个预设部位的照片归一化为第一预设像素的背景色为黑色的照片；
    将该待识别牲畜的归一化后的各个预设部位的照片按照预先确定的拼接规则拼接成该待识别牲畜的待调整拼接照片；
    将该待调整拼接照片重置为第二预设像素的该待识别牲畜的拼接照片。
  13. 如权利要求8所述的电子装置，其特征在于，所述待验证的身份标识对应的牲畜识别模型的训练过程包括如下步骤：
    A1、获取第一预设数量的某种牲畜的各个预设部位的照片样本，为每头牲畜分配一个唯一的身份标识，其中，该种牲畜与所述待识别牲畜的种类相同，分配的身份标识中包括所述待验证的身份标识；
    A2、将所述照片样本随机组合，生成多个照片样本组合，其中，每个照片样本组合包括所有所述预设部位的照片样本，且每个照片样本组合包括的各个预设部位的照片样本的数量为1；
    A3、将各个照片样本组合中的照片样本按照预先确定的拼接规则拼接成待模型训练的拼接照片样本，以各个照片样本均属于所述待验证的身份标识对应牲畜的拼接照片样本作为正样本，以其他拼接照片样本作为负样本；
    A4、将所述拼接照片样本分为第一预设比例的训练集和第二预设比例的验证集，利用训练集中的各个拼接照片样本对所述牲畜识别模型进行训练，并在训练完成后利用验证集中的各个拼接照片样本对该牲畜识别模型的准确率进行验证；
    A5、若准确率大于预设阈值，则训练过程结束，若准确率小于或等于预设阈值，则增加所述拼接照片样本的数量，并基于增加的拼接照片样本重新执行步骤A4。
  14. 如权利要求8所述的电子装置，其特征在于，所述牲畜识别模型为卷积神经网络模型。
  15. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质中包括牲畜识别程序，所述牲畜识别程序被处理器执行时，实现如下步骤：
    接收步骤：接收待识别牲畜的各个预设部位的照片和相应的待验证的身份标识；
    拼接步骤：按照预先确定的拼接规则将该待识别牲畜的各个预设部位的照片拼接成该待识别牲畜的拼接照片；
    确定步骤：根据所述待验证的身份标识与牲畜识别模型的映射关系，确定该身份标识对应的牲畜识别模型；及
    识别步骤：将所述待识别牲畜的拼接照片输入确定的预先训练得到的牲畜识别模型，输出识别结果。
  16. 如权利要求15所述的计算机可读存储介质，其特征在于，所述预设部位包括牲畜的脸部、耳部、蹄部和尾部。
  17. 如权利要求15所述的计算机可读存储介质，其特征在于，所述预先确定的拼接规则包括：
    牲畜的脸部照片在所述拼接照片的左上方；
    牲畜的耳部照片在所述拼接照片的右上方；
    牲畜的蹄部照片在所述拼接照片的左下方；
    牲畜的尾部照片在所述拼接照片的右下方。
  18. 如权利要求16所述的计算机可读存储介质，其特征在于，所述预先确定的拼接规则包括：
    牲畜的脸部照片在所述拼接照片的左上方；
    牲畜的耳部照片在所述拼接照片的右上方；
    牲畜的蹄部照片在所述拼接照片的左下方；
    牲畜的尾部照片在所述拼接照片的右下方。
  19. 如权利要求16所述的计算机可读存储介质，其特征在于，所述拼接步骤包括：
    分别将所述待识别牲畜的各个预设部位的照片归一化为第一预设像素的背景色为黑色的照片；
    将该待识别牲畜的归一化后的各个预设部位的照片按照预先确定的拼接规则拼接成该待识别牲畜的待调整拼接照片；
    将该待调整拼接照片重置为第二预设像素的该待识别牲畜的拼接照片。
  20. 如权利要求16所述的计算机可读存储介质，其特征在于，所述待验证的身份标识对应的牲畜识别模型的训练过程包括如下步骤：
    A1、获取第一预设数量的某种牲畜的各个预设部位的照片样本，为每头牲畜分配一个唯一的身份标识，其中，该种牲畜与所述待识别牲畜的种类相同，分配的身份标识中包括所述待验证的身份标识；
    A2、将所述照片样本随机组合，生成多个照片样本组合，其中，每个照片样本组合包括所有所述预设部位的照片样本，且每个照片样本组合包括的各个预设部位的照片样本的数量为1；
    A3、将各个照片样本组合中的照片样本按照预先确定的拼接规则拼接成待模型训练的拼接照片样本，以各个照片样本均属于所述待验证的身份标识对应牲畜的拼接照片样本作为正样本，以其他拼接照片样本作为负样本；
    A4、将所述拼接照片样本分为第一预设比例的训练集和第二预设比例的验证集，利用训练集中的各个拼接照片样本对所述牲畜识别模型进行训练，并在训练完成后利用验证集中的各个拼接照片样本对该牲畜识别模型的准确率进行验证；
    A5、若准确率大于预设阈值，则训练过程结束，若准确率小于或等于预设阈值，则增加所述拼接照片样本的数量，并基于增加的拼接照片样本重新执行步骤A4。
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