KR102609461B1 - Imaging device, apparatus and method for managing of fowl comprising the same - Google Patents

Abstract

According to an embodiment of the present invention, an image sensor that captures an image and generates image data; a data processing unit that detects an object using the image data, determines a head portion of the object, and generates distribution data for the head portion; and a communication unit that transmits the image data and the distribution data to a management server.

Description

IMAGING DEVICE, APPARATUS AND METHOD FOR MANAGING OF FOWL COMPRISING THE SAME}

One embodiment of the present invention relates to an imaging device and a poultry management system and method including the same.

As consumption of livestock products increases, various ways to manage livestock efficiently are being proposed.

Conventionally, people would check the condition of each livestock, and receive medical treatment or isolate livestock that showed abnormal signs. However, when there are a large number of livestock, such as poultry, there is a problem that a lot of manpower is needed to manage all the livestock.

In addition, if an infectious disease occurs in some livestock, it may result in the death of most of the livestock, so biosecurity and preventive measures that can quickly and accurately determine whether there is an abnormality in livestock while minimizing human access are required. there is.

In addition, due to the development of means of transportation, diseases that occur in a specific region are not limited to a specific location and are spread nationwide in a short period of time, so quick disease diagnosis is required at an early stage. Currently, in order to determine diseases in livestock, farm owners or general workers patrol periodically to check the condition of farmed livestock and determine whether there is a disease based on subjective judgment. However, this method has the disadvantage that the judgment criteria vary depending on the individual experience and capabilities of the farm owner or worker and that accuracy is low. In addition, there is a problem that frequent entry into the breeding farm may cause the spread of infectious diseases.

The technical problem to be achieved by the present invention is to provide an imaging device capable of detecting individuals likely to have a disease from image data captured inside a breeding farm, and a poultry analysis system and method including the same.

According to an embodiment of the present invention, an image sensor that captures an image and generates image data; a data processing unit that detects an object using the image data, determines a head portion of the object, and generates distribution data for the head portion; and a communication unit that transmits the image data and the distribution data to a management server.

The distribution data may include at least one of a heat map and coordinate information about the head portion.

The data processing unit may detect the color of a position corresponding to the head unit on the image data using the distribution data and generate color data for the head unit.

The data processing unit may divide the image data into a plurality of zones and generate color data for each zone for the head unit.

The communication unit may transmit at least one of the color data and the color data for each zone to the management server.

It may further include a control unit that determines the health status of the poultry using at least one of the color data and the color data for each zone.

The control unit may determine the health status of the poultry by comparing the color data with a preset threshold range.

According to an embodiment of the present invention, an image sensor that captures an image and generates image data; a data processing unit that determines a head portion of the object using the image data and generates distribution data for the head portion; and an imaging device including a communication unit that transmits the image data and the distribution data; and a management server that receives the image data and the distribution data, detects the color of the position corresponding to the head portion in the image data using the distribution data, and analyzes the detected color to determine the health status of the poultry. Provides a poultry management system including.

According to an embodiment of the present invention, capturing an image to generate image data; detecting an object from the image data; determining the head portion of the object; generating distribution data for the head portion; detecting the color of a position corresponding to the head portion on the image data using the distribution data; Provides a poultry management method including the step of determining the health status of poultry by analyzing the detected color.

The poultry analysis device and imaging device of the present invention can detect abnormal individuals that are highly likely to have a disease on image data captured inside a breeding farm.

Additionally, abnormal objects can be tracked and monitored.

Additionally, when an abnormal object occurs, a notification or warning can be sent to the administrator.

Additionally, the amount of calculation of analysis data in the management server can be reduced and calculation speed can be improved.

1 is a conceptual diagram of a breeding farm management system according to an embodiment of the present invention.
Figure 2 is a block diagram of an imaging device according to an embodiment of the present invention.
Figure 3 is a conceptual diagram of a heat map according to an embodiment of the present invention.
Figure 4 is a conceptual diagram of coordinate information according to an embodiment of the present invention.
Figure 5 is a conceptual diagram of color data according to an embodiment of the present invention.
Figure 6 is a conceptual diagram of color data for each zone according to an embodiment of the present invention.
Figure 7 is a diagram for explaining poultry health status determination using color data according to an embodiment of the present invention.
Figure 8 is a diagram illustrating the determination of poultry health status using color data determination for each zone according to an embodiment of the present invention.
Figure 9 is a conceptual diagram for explaining the operation of a poultry management system according to an embodiment of the present invention.
10 to 13 are flowcharts of a poultry management method according to an embodiment of the present invention.

Since the present invention can be subject to various changes and can have various embodiments, specific embodiments will be illustrated and described in the drawings. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all changes, equivalents, and substitutes included in the spirit and technical scope of the present invention.

Terms containing ordinal numbers, such as second, first, etc., may be used to describe various components, but the components are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another. For example, the second component may be referred to as the first component without departing from the scope of the present invention, and similarly, the first component may also be referred to as the second component. The term and/or includes any of a plurality of related stated items or a combination of a plurality of related stated items.

When a component is said to be "connected" or "connected" to another component, it is understood that it may be directly connected to or connected to the other component, but that other components may exist in between. It should be. On the other hand, when it is mentioned that a component is “directly connected” or “directly connected” to another component, it should be understood that there are no other components in between.

The terms used in this application are only used to describe specific embodiments and are not intended to limit the invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the technical field to which the present invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and unless explicitly defined in the present application, should not be interpreted in an ideal or excessively formal sense. No.

Hereinafter, embodiments will be described in detail with reference to the attached drawings, but identical or corresponding components will be assigned the same reference numbers regardless of reference numerals, and duplicate descriptions thereof will be omitted.

Figure 1 is a conceptual diagram of a breeding farm according to an embodiment of the present invention, and Figure 2 is a block diagram of an imaging device according to an embodiment of the present invention.

Referring to Figures 1 and 2, first, the breeding farm 10 refers to a barn for raising livestock. Livestock may include not only poultry such as chickens and ducks, but also various types of animals raised in groups in livestock farms, such as cows and pigs.

The imaging device 100 may detect the environment within the breeding farm 10 and transmit it to at least one of the management server 200 and the air conditioning device 300. To this end, the imaging device 100 may communicate with the management server 200 and the air conditioning device 300 by wire or wirelessly. Here, the imaging device 100 is shown to communicate with the management server 200 and the air conditioning device 300, respectively, but is not limited to this, and the imaging device 100 communicates with the management server 200, and the management server (200) may communicate with the air conditioning device (300).

A plurality of imaging devices may be disposed within the breeding farm 10. The imaging device 100 may transmit image data captured by the image sensor 111 to the management server 200. Alternatively, a separate data collection device 150 may be placed in the breeding farm 10 to collect image data from a plurality of imaging devices 100 and transmit it to the management server 200. In order to precisely analyze the condition inside the breeding farm 10, the image data of the imaging device 100 may have high-definition and high-capacity characteristics. Therefore, in order to transmit image data captured by a plurality of imaging devices 100 to the remote management server 200 in real time, a large bandwidth may be required, and accordingly, the transmission speed may decrease. Therefore, the separate data collection device 150 collects image data from the imaging device 100 in the breeding farm 10, encodes it, and then transmits it to the management server 200, thereby reducing communication bandwidth and improving transmission speed. there is. Alternatively, the imaging device 100 may perform primary analysis to analyze the state of the breeding farm 10 using image data and then transmit the processed data to the data collection device or to the management server 200. That is, the imaging device 100 can efficiently manage communication resources by selecting only the data necessary for analyzing the state of the breeding farm 10 and transmitting it to the data collection device 150 or the management server 200. Alternatively, the imaging device 100 primarily analyzes the internal state of the breeding farm 10 using image data captured in the breeding farm 10 and transmits the results to the data collection device 150 or the management server 200. Can be transmitted.

The management server 200 may be a server including a personal computer (PC), tablet PC, mobile terminal, etc. When the imaging device 100 transmits the environment in the kennel 10 to the management server 200, the manager can recognize the environment in the kennel 10 through a screen output to the management server 200. For example, when the imaging device 100 captures an abnormal situation in the breeding farm 10 and transmits it to the management server 200, the manager can detect the abnormality in the breeding farm 10 through the screen displayed on the management server 200. You can recognize that a situation has occurred and respond early to abnormal situations. Here, the abnormal situation may be, for example, the occurrence of diseased livestock, pregnancy of livestock, growth period of livestock, humidity, temperature, concentration of a specific molecule, etc. in the breeding farm 10.

Additionally, the management server 200 may analyze the condition of the breeding farm using data transmitted from the imaging device 100 or the data collection device 150. For example, the management server 100 may receive primarily processed data from a plurality of imaging devices 100 or data collection devices 150 to perform a more precise analysis of the state of the breeding farm. The management server 200 may perform data communication with the communication unit 114 of the imaging device 100 or may perform data communication with a communication module (not shown) mounted on the data collection device 150. The management server 200 may receive the original image captured by the imaging device 100 and the primarily processed image data. At this time, the processed image data may refer to data in which the kennel management device 100 or the imaging device 100 performs primary analysis on the original image and encodes it.

The air conditioning device 300 is a device that controls the temperature of the breeding farm 10. When the imaging device 100 captures a temperature abnormality in the breeding farm 10 and transmits it to the management server 200, the manager reports that a temperature abnormality has occurred in the breeding farm 10 through the screen displayed on the management server 200. It can be recognized, and the temperature in the breeding farm 10 can be normalized by controlling the air conditioning device 300. Alternatively, when the imaging device 100 detects a temperature abnormality in the breeding farm 10 and directly transmits the detected temperature to the air conditioning device 300, the air conditioning device 300 may directly normalize the temperature in the breeding farm 10. For example, if the temperature in the breeding farm 10 is lower or higher than the temperature appropriate for livestock living, the livestock's movement tends to slow down. Accordingly, the imaging device 100, the management server 200, or the air conditioning device 300 can detect a temperature abnormality within the breeding farm 10 and normalize the temperature within the breeding farm 10.

The imaging device 100 includes an image sensor 111, a data processing unit 112, a motion detection unit 113, a control unit 113, a communication unit 114, a display unit 115, a user interface unit 116, and an encoding unit 117. ), database 118, light source unit 119, and pan/tilt unit 120.

A plurality of imaging devices 100 may be installed in the breeding farm 10. For example, the imaging device 100 may include at least one upper imaging device disposed on the upper part of the kennel 10 and at least one side imaging device disposed on the side of the kennel 10. Each of the upper imaging device and the side imaging device may be an IP camera capable of transmitting real-time images through wired or wireless communication. In an embodiment of the present invention, an example will be given where one imaging device 100 is disposed at the top of a breeding farm.

The image sensor 111 may generate image data by capturing an image including a plurality of objects. In an embodiment of the present invention, a plurality of individuals may refer to poultry raised inside a breeding farm. The image sensor 111 may be an image sensor that photographs a subject using a complementary metal-oxide semiconductor (COMS) module or a charge coupled device (CCD) module. At this time, the input image frame is provided to the COMS module or CCD module in the image sensor 111 through the lens, and the COMS module or CCD module converts the optical signal of the subject that has passed through the lens into an electrical signal (image data) and outputs it. do.

The image sensor 111 may include a fisheye lens or a wide-angle lens with a wide viewing angle. Accordingly, it is possible for one imaging device 100 to photograph the entire space inside the breeding farm 10.

Additionally, the image sensor 111 may be a depth camera. The image sensor 111 may be driven by any one of various depth recognition methods, and an image captured through the image sensor 111 may include depth information. The image sensor 111 may be, for example, a Kinect sensor. The Kinect sensor is a structured light projection depth camera that can obtain three-dimensional information of a scene by projecting a defined pattern image using a projector or laser and acquiring the projected image of the pattern through the camera. This Kinect sensor includes an infrared emitter that irradiates a pattern using an infrared laser, and an infrared camera that takes infrared images, and an RGB camera that functions like a general webcam is placed between the infrared emitter and the infrared camera. In addition, the Kinect sensor may further include a pan/tilt unit 120 that adjusts the angle of the microphone array and camera.

The basic principle of the Kinect sensor is that when a laser pattern emitted from an infrared emitter is projected and reflected on an object, the distance to the object surface is calculated using the position and size of the pattern at the reflection point. According to this principle, the image sensor 111 can generate image data including depth information for each object by irradiating a laser pattern into the space within the livestock barn and sensing the laser pattern reflected from the object.

The data processing unit 112 may detect an object from image data, determine the head and body of the object, and generate distribution data for the head. In an embodiment of the present invention, the head part may refer to a set of pixels constituting the head of an object, and the body part may refer to a set of pixels constituting the body of the object. For example, if the object is a chicken, the head part may refer to the head including the crest.

For example, the data processing unit 112 detects the outline of an object from image data, compares the detected outline with the outline of the animal object pre-stored in the database 118, and creates an outline that matches the outline of the pre-stored animal object. An entity with an animal can be detected as an animal entity. At this time, the appearance of the animal entity stored in the database 118 may be that of at least one animal entity, and the data processing unit 112 detects the entity with the matching outline as the animal entity as described above and at the same time The type of animal can also be determined.

In addition, for example, the data processing unit 112 extracts the feature points of an object in the image data, and if the extracted feature point matches the feature point of the animal object previously stored in the database 118 with a proximity of more than a threshold value, the data processing unit 112 extracts the feature point of the object in the image data. The entity can be detected as an animal entity. At this time, the data processing unit 112 extracts feature points from the images of the two objects being compared, and performs SIFT (Scale Invariant Feature Transform) or SURF (Speeded Up Robust Features) matching feature point descriptors of the two extracted objects. Algorithms can be used.

Additionally, for example, the data processing unit 112 may detect animal entities based on the outlines of the entities in the image data. More specifically, the data processing unit 112 detects the outlines of objects in the image data to generate an edge image, and detects the outline from the foreground image data, which is the background image of the breeding farm pre-stored in the database 118, to create a background edge image. Animal objects can be detected in a different image obtained by subtracting the background edge image from the edge image. At this time, the data processing unit 112 generates an edge image by detecting the outline of an object appearing within the frame as an edge using the gradient information of the image data frame. Here, gradient information is a value generated from difference values between adjacent pixels among predetermined pixels in a frame and means the sum of the absolute values of the differences, and edge means a boundary line between objects using gradient information.

Additionally, the data processing unit 112 may generate a background edge image by detecting the edge of an object corresponding to the background from previously photographed image data of the foreground in the breeding farm. At this time, the background edge image may be an image in which the outlines of objects in a preset area are detected as background edges, but the same is repeated a predetermined number of times by comparing a plurality of image data frames of the foreground within the previously photographed breeding farm 10. It may be an image in which the outline of an object that appears is detected as a background edge.

Additionally, the data processing unit 112 may detect animal entities in image data using an entity detection classifier. At this time, the object detection classifier is learned by building a training database from images of animal objects taken with different postures or external environments. This object detection classifier uses SVM (Support Vector Machine), neural network, and AdaBoost algorithm. A DB of animal entities is created through various learning algorithms, including: Specifically, the data processing unit 112 detects the edge of an object corresponding to the foreground from the image data of the background in the previously photographed breeding farm, applies the edge of the foreground object detected in the image data, and generates an image to which the edge of the foreground object is applied. Animal entities can be detected by applying an entity detection classifier to the data area.

In addition, the data processing unit 112 reduces noise in image data captured by the image sensor 111, performs gamma correction, color filter array interpolation, color matrix, and color Image signal processing to improve image quality, such as color correction and color enhancement, can be performed. The data processing unit 112 may also perform color processing, blur processing, edge emphasis processing, image analysis processing, image recognition processing, and image effect processing.

The data processing unit 112 may determine the head portion and body portion of the object using the detected entity data and generate distribution data for the head portion.

The data processing unit 112 may compare the outline of the detected object with the external appearance of the poultry previously stored in the database 118 to determine the head and body portions of the poultry. The data processing unit 112 may determine the head portion and the body portion and then detect only the head portion to generate distribution data for the head portion.

Alternatively, if the extracted feature point matches the feature point of the poultry head part previously stored in the database 118 with a proximity greater than or equal to a threshold, the data processing unit 112 may detect the head part in the corresponding image data.

In an embodiment of the present invention, the distribution data for the head portion may mean data representing the position information of the head portion excluding the body portion in the image data. In an embodiment of the present invention, distribution data for the head portion may be expressed in at least one of a heat map and coordinate information for the head portion.

Figure 3 is a conceptual diagram of a heat map according to an embodiment of the present invention. Referring to Figures 2 and 3, the data processing unit 112 detects all objects present in the image data and determines the head and body of each object. The data processing unit 112 uses the detection result to generate a heat map including only the head portion. In Figure 3, the left side represents image data, and the right side represents a heat map of the head expressed using image data. The data processing unit 112 generates a heat map on the right using the head portion of the image data excluding the body portion.

The heat map may be displayed as a set of pixels corresponding to the head portion, a block containing pixels, a set of blocks, etc. Each pixel, block, etc. may be displayed as 0/1 indicating the presence or absence of a head, or may contain values of at least 3 levels or more depending on the probability of existence.

The right side of FIG. 3 may mean, for example, that when the value of the pixel corresponding to the head is 1, it is displayed in a different color than the pixel whose value is 0.

Figure 4 is a conceptual diagram of coordinate information according to an embodiment of the present invention. Referring to Figures 2 and 4, the data processing unit 112 detects all objects present in the image data and determines the head and body of each object. The data processing unit 112 uses the detection result to generate coordinate information including two-dimensional coordinates of the head portion and width and height information of the area including the head portion.

The data processing unit 112 may generate color data for the head portion by detecting the color of a position corresponding to the head portion in the image data using distribution data. The data processing unit 112 may detect the color of each pixel constituting the head portion by analyzing the pixel corresponding to the position of the head portion in the original image data using the distribution data of the head portion. For example, color data may refer to the RGB color channel for each pixel constituting the head unit.

Figure 5 is a conceptual diagram of color data according to an embodiment of the present invention. Referring to Figures 2 and 5, the data processing unit 112 generates color data for the head part by calculating an RGB color channel for each pixel constituting the head part. In an embodiment of the present invention, color data may mean RGB color channel information for each pixel.

Alternatively, the data processing unit 112 may divide the image data into a plurality of zones and generate color data for each zone for the head portion. For example, color data for each region may mean the average value or sum of head colors that exist within a specific region in the image data. There are hundreds or even tens of thousands of poultry distributed within the farm. Therefore, when image data taken from a breeding farm is divided into a plurality of zones and color data for each zone is generated, the probability of diseased poultry being located in each zone can be predicted.

Figure 6 is a conceptual diagram of color data for each zone according to an embodiment of the present invention. Referring to Figures 2 and 6, the data processing unit 112 calculates an RGB color channel for each head part existing in each zone, averages the calculated RGB color channels for each zone, and calculates color data for each zone. Alternatively, the data processing unit 112 calculates an RGB color channel for each head unit existing in each zone, and adds the calculated RGB color channels for each zone to calculate color data for each zone. In an embodiment of the present invention, color data for each region may mean RGB color channel information averaged for each region.

The control unit 113 may determine the health status of poultry using at least one of color data and color data for each zone.

The control unit 113 may determine the health status of the poultry by comparing the color data with a preset critical range. Figure 7 is a diagram for explaining poultry health status determination using color data according to an embodiment of the present invention. Referring to Figures 2 and 7, the control unit 113 compares the RGB color channel for each pixel with a preset threshold range and detects pixels included in the threshold range. At this time, the critical range may be a group of color channels including blue to green colors in the RGB color channel. If the control unit finds a pixel that falls within the threshold range, it can determine the object to be an abnormal object.

Alternatively, the control unit 113 may determine the health status of poultry using color data for each zone. Figure 8 is a diagram illustrating the determination of poultry health status using color data determination for each zone according to an embodiment of the present invention. Referring to Figures 2 and 8, the control unit 113 first divides the image data into a plurality of zones. At this time, the plurality of zones may have the same size, but are not limited thereto. The control unit 113 detects the RGB color channels of each head unit and averages or adds up the RGB color channels of the head unit for each zone. Each RGB color channel can be composed of a matrix expressed as [Red (0~255), Green (0~255), Blue (0~255)], and averaging and summing of the RGB channels uses the four arithmetic operations of the matrix. It can be performed through. That is, averaging and summing of the RGB channels can be performed through matrix operations on the RGB color channels expressed as a 3 x 1 matrix (Red, Green, Blue). Since the pixels that make up the head of a poultry include the comb and face, the average or summed value of the RGB channels must be filtered to filter out the color of the pixels that make up the face. Generally, chicken combs are red, and the combs of diseased chickens are blue to green. Accordingly, the data processing unit 112 filters the remaining RGB channels except for the red, blue, and green RGB channels, thereby removing the remaining information, excluding the comb portion for determining the health status of the poultry, as noise. That is, the data processing unit 112 can noise-process the pixels constituting the remaining area of the head area excluding the comb and calculate the average or summed value of the RGB channels using only the pixels constituting the crest. The control unit 113 determines the health status of poultry for each zone by comparing the calculated average or sum value of the RGB channels with a preset threshold range.

Alternatively, the control unit 113 may determine the health status of the poultry using color data and color data for each zone. The control unit 113 primarily uses color data for each zone to determine whether the average or sum of RGB channels in a specific zone is within a preset first threshold range. The first critical range is a list containing RGB color channel values for determining whether there is an abnormality in color data (RGB color channel) for each zone. For a specific area included in a preset critical range, the control unit 113 secondarily calculates color data for the head portion present in the area, determines whether each pixel is included in the second critical range, and determines the health status of the poultry. can be judged. The second threshold range is a list containing RGB color channel values for determining whether there is an abnormality in the color data (RGB color channel) corresponding to one pixel.

Additionally, the control unit 113 may execute various commands for operating the imaging device 100.

The communication unit 114 may perform data communication with another imaging device, the data collection device 150, or the management server 200. For example, the communication unit 114 supports wireless LAN (Wireless LAN), Wi-Fi, Wibro (Wireless Broadband), World Interoperability for Microwave Access (Wimax), and High Speed Downlink (HSDPA). Data communication can be performed using long-distance communication technologies such as Packet Access, IEEE 802.16, Long Term Evolution (LTE), and Wireless Mobile Broadband Service (WMBS).

Alternatively, the communication unit 114 may include Bluetooth, RadioFrequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wideband (UWB), ZigBee, Near Field Communication (NFC), etc. Additionally, as a wired communication technology, data communication can be performed using short-distance communication technologies such as USB communication, Ethernet, serial communication, and optical/coaxial cables.

For example, the communication unit 114 may perform data communication with another imaging device or data collection device 150 using short-distance communication technology, and may perform data communication with the management server 200 using long-distance communication technology. However, the communication technology is not limited to this, and various communication technologies may be used in consideration of all matters of the breeding farm 10.

The communication unit 114 may transmit image data and distribution data to the data collection device 150 or the management server 200.

Alternatively, the communication unit 114 may transmit at least one of color data and color data for each zone to the data collection device 150 or the remote management server 200.

The management server 200 collects image data from a plurality of imaging devices 100 and analyzes the health status of poultry. At this time, when the management server 200 detects the head portion from a plurality of image data and analyzes the color data, the data throughput increases exponentially, which may significantly reduce the processing speed. Therefore, in an embodiment of the present invention, the load on the management server can be reduced by performing the task of detecting the head or analyzing color data in each imaging device and transmitting the task to the management server.

In an embodiment of the present invention, analysis of the health status of poultry may be performed in at least one of the imaging device 100 and the management server 200. That is, the imaging device 100 can generate image data and transmit it to the management server 200, and the management server 200 can generate distribution data, detect color data, and analyze poultry health status. According to this method, the imaging device 100 does not need a separate process for analyzing image data, and the management server 200 analyzes the health status of poultry by analyzing image data collected from a plurality of imaging devices. .

Alternatively, if the imaging device 100 generates image data and distribution data for the head is transmitted to the management server 200, the management server 200 can detect color data and perform poultry health status analysis. . According to this method, the data processing amount of the management server 200 can be reduced because the imaging device 100 performs the process for generating distribution data of the head portion. Additionally, data loss may occur due to compression in the process of transmitting image data from the imaging device 100 to the management server 200. When generating distribution data in the imaging device, the head portion is detected using lossless image data, and Since distribution data is generated for this, accuracy can be improved compared to the case where the management server 200 generates distribution data using image data that has lost due to compression.

Alternatively, if the imaging device 100 generates image data, generates distribution data, and detects color data and transmits it to the management server 200, the management server 200 can perform poultry health status analysis using the color data. . According to this method, the amount of data calculation can be further reduced because the management server 200 only performs poultry health status analysis using color data.

Alternatively, the imaging device 100 may generate image data, generate distribution data, detect color data, and analyze poultry health status, and transmit the analysis results to the management server 200. According to this method, because the imaging device 100 performs all processes for poultry analysis, the management server 200 can only perform the function of collecting and managing poultry analysis results from a plurality of imaging devices 200.

Data transmitted through the communication unit 114 may be compressed data encoded through the encoding unit 117.

The display unit 115 includes a liquid crystal display (LCD), a thin film transistor-liquid crystal display (TFT LCD), an organic light-emitting diode (OLED), and a flexible display. ), a 3D display, and an e-ink display.

The display unit 115 may display at least one of distribution data, color data, and color data through the control unit 113.

Additionally, the display unit 115 may output original image data captured by the image sensor 111 on the screen.

Additionally, the display unit 115 can output various user interfaces or graphic user interfaces on the screen.

The user interface unit 116 generates input data for controlling the operation of the imaging device 100. The user interface unit 116 may be composed of a key pad, dome switch, touch pad, jog wheel, jog switch, etc. When the display unit 115 and the touch pad form a layered structure to form a touch screen, the display unit 115 can be used as an input device in addition to an output device.

The user interface unit 116 may receive various commands for operating the imaging device.

The encoding unit 117 encodes original image data captured by the image sensor 111 or processed image data processed through the data processing unit 112, motion detection unit 113, and control unit 113 into a digital signal. For example, the encoding unit 117 may encode image data according to H.264, H.265, MPEG (Moving Picture Experts Group), and M-JPEG (Motion Joint Photographic Experts Group) standards.

The database 118 is a flash memory type, hard disk type, multimedia card micro type, and card type memory (for example, SD or XD memory, etc.). ), magnetic memory, magnetic disk, optical disk, RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory: ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM ( It may include at least one storage medium among Programmable Read-Only Memory. Additionally, the imaging device 100 may operate web storage that performs a storage function of the database 118 on the Internet, or may operate in relation to web storage.

The database 118 can store image data captured by the image sensor 111 and can store image data for a certain period of time in the past.

Additionally, the database 118 may store distribution data, color data, and color data for each zone generated through the data processing unit 112.

Additionally, the database 118 may store data and programs necessary for the imaging device 100 to operate.

Additionally, the database 118 may store various user interfaces (UI) or graphic user interfaces (GUI).

The light source unit 119 may radiate light in a direction under the control of the control unit 113. For example, the light source unit 119 may include at least one laser diode (LD) or light emitting diode (LED). The light source unit can irradiate light of various wavelengths under the control of the control unit.

For example, the light source unit 119 may irradiate light in the infrared wavelength range for night photography. Alternatively, the light source unit 119 may irradiate light in the ultraviolet wavelength range for photochemotherapy of livestock in the farm.

Figure 9 is a conceptual diagram for explaining the operation of a poultry management system according to an embodiment of the present invention.

Referring to FIG. 9, the management server 200 receives image data and distribution data from a plurality of imaging devices 100, detects the color of the position corresponding to the head in the image data using the distribution data, and detects By analyzing the color, the health status of poultry can be determined. In an embodiment of the present invention, distribution data for the head portion may mean data containing location information of the head portion excluding the body portion in image data. In an embodiment of the present invention, distribution data for the head portion may be expressed in at least one of a heat map and coordinate information for the head portion.

According to this, the imaging device 100 generates distribution data containing the location information of the head portion and transmits it to the management server 200, so that the location of the head portion is detected by the imaging device 100 and the management server 200 The accuracy of analysis can be improved through distributed processing that analyzes the health status of poultry using head distribution data.

Alternatively, the management server 200 may determine the health status of poultry by receiving at least one of color data and color data for each zone from the plurality of imaging devices 100. According to this, the imaging device 100 generates distribution data containing the location information of the head, generates color data, and transmits it to the management server 200, so that the management server 200 performs the process of detecting the head and the color. The separate calculation process for detection can be omitted. The management server 200 can quickly analyze the health status of poultry using the received color data. Accordingly, the amount of computation of the management server 200 is greatly reduced, and the load on the management server that collects and analyzes a large number of image data from the plurality of imaging devices 100 can be reduced.

Figure 10 is a flow chart of a poultry management method according to an embodiment of the present invention. Referring to Figure 10, first, the image sensor captures an image including a plurality of objects. The image sensor is placed inside the breeding farm to capture images of multiple objects, including poultry, and transmit them to the data processing unit (S1001).

Next, the image sensor generates image data using an image containing multiple objects. One image data may mean a single frame. The image sensor generates a plurality of image data using sequentially captured images (S1002).

Next, the data processing unit detects objects in the image data. The data processing unit detects the object using the characteristic points of the object, the outline of the object, and the outline of the object. Alternatively, the data processing unit detects objects in image data using various learning algorithms including SVM (Support Vector Machine), neural network, and AdaBoost algorithm (S1003).

Next, the data processing unit uses the detected object data to determine the head and body of the object and generates distribution data for the head. The distribution data includes at least one of a heat map and coordinate information for the head portion (S1004).

Next, the data processing unit detects the color of the position corresponding to the head portion in the image data using the distribution data. The data processing unit detects the color of a position corresponding to the head in the image data using distribution data and generates color data for the head. The data processing unit detects the color of each pixel constituting the head portion by analyzing the pixels corresponding to the position of the head portion in the original image data using the distribution data of the head portion. For example, color data refers to the RGB color channel for each pixel that makes up the head part. Alternatively, the data processing unit divides the image data into a plurality of zones and generates color data for each zone for the head portion (S1005).

Next, the control unit analyzes the detected color to determine the health status of the poultry. The control unit determines the health status of poultry using color data or color data by zone. At this time, the control unit determines the health status of the poultry by comparing the color data with a preset critical range (S1006).

Figure 11 is a flowchart of a poultry management method according to an embodiment of the present invention. Referring to Figure 11, first, the image sensor captures an image including a plurality of objects. Image sensor (S1101).

Next, the photographing unit generates image data using an image containing a plurality of objects. One image data may mean a single frame. An image sensor generates a plurality of image data using sequentially captured images (S1102).

Next, the data processing unit detects objects in the image data. The data processing unit detects the object using the characteristic points of the object, the outline of the object, and the outline of the object. Alternatively, the data processing unit detects objects in image data using various learning algorithms including SVM (Support Vector Machine), neural network, AdaBoost algorithm, etc. (S1103).

Next, the data processing unit uses the detected object data to determine the head and body of the object and generates distribution data for the head. The distribution data includes at least one of a heat map and coordinate information for the head portion (S1104).

Next, the communication unit transmits the image data and distribution data to the management server (S1105).

Next, the management server detects the color of the position corresponding to the head in the image data using the distribution data. The management server detects the color of a position corresponding to the head in the image data using distribution data and generates color data for the head. The management server detects the color of each pixel constituting the head portion by analyzing the pixel corresponding to the position of the head portion in the original image data using the head portion distribution data. For example, color data refers to the RGB color channel for each pixel that makes up the head part. Alternatively, the management server divides the image data into a plurality of zones and generates color data for each zone for the head portion (S1106).

Next, the management server analyzes the detected color to determine the health status of the poultry. The management server determines the health status of poultry using color data or color data by zone. At this time, the management server determines the health status of the poultry by comparing the color data with a preset critical range (S1107).

Figure 12 is a flowchart of a poultry management method according to an embodiment of the present invention. Referring to Figure 12, first, the image sensor captures an image including a plurality of objects. Image sensor (S1201).

Next, the image sensor generates image data using an image containing multiple objects. One image data may mean a single frame. The image sensor generates a plurality of image data using sequentially captured images (S1202).

Next, the data processing unit detects objects in the image data. The data processing unit detects the object using the characteristic points of the object, the outline of the object, and the outline of the object. Alternatively, the data processing unit detects objects in image data using various learning algorithms including SVM (Support Vector Machine), neural network, AdaBoost algorithm, etc. (S1203).

Next, the data processing unit uses the detected object data to determine the head and body of the object and generates distribution data for the head. The distribution data includes at least one of a heat map and coordinate information for the head portion (S1204).

Next, the data processing unit detects the color of the position corresponding to the head portion in the image data using the distribution data. The data processing unit detects the color of a position corresponding to the head in the image data using distribution data and generates color data for the head. The data processing unit detects the color of each pixel constituting the head portion by analyzing the pixels corresponding to the position of the head portion in the original image data using the distribution data of the head portion. For example, color data refers to the RGB color channel for each pixel that makes up the head part. Alternatively, the data processing unit divides the image data into a plurality of zones and generates color data for each zone for the head portion (S1205).

Next, the communication unit transmits at least one of color data and color data for each zone to the management server (S1206).

Next, the management server analyzes the detected color to determine the health status of the poultry. The management server determines the health status of poultry using color data or color data by zone. At this time, the management server determines the health status of the poultry by comparing the color data with a preset critical range (S1207).

Figure 13 is a flowchart of a poultry management method according to an embodiment of the present invention. Referring to Figure 13, first, the image sensor captures an image including a plurality of objects (S1301).

Next, the image sensor generates image data using an image containing multiple objects. One image data may mean a single frame. The image sensor generates a plurality of image data using sequentially captured images (S1302).

Next, the communication unit transmits the video data to the management server (S1303).

Next, the management server detects objects in the video data. The management server detects the object using the characteristic points of the object, the outline of the object, and the outline of the object. Alternatively, the data processing unit detects objects in image data using various learning algorithms including SVM (Support Vector Machine), neural network, and AdaBoost algorithm (S1304).

Next, the management server uses the detected entity data to determine the head and body of the entity and generates distribution data for the head. The distribution data includes at least one of a heat map and coordinate information for the head portion (S1305).

Next, the management server detects the color of the position corresponding to the head in the image data using the distribution data. The management server detects the color of a position corresponding to the head in the image data using distribution data and generates color data for the head. The management server detects the color of each pixel constituting the head portion by analyzing the pixel corresponding to the position of the head portion in the original image data using the head portion distribution data. For example, color data refers to the RGB color channel for each pixel that makes up the head part. Alternatively, the management server divides the image data into a plurality of zones and generates color data for each zone for the head portion (S1306).

Next, the management server analyzes the detected color to determine the health status of the poultry. The management server determines the health status of poultry using color data or color data by zone. At this time, the management server determines the health status of the poultry by comparing the color data with a preset critical range (S1307).

The term '~unit' used in this embodiment refers to software or hardware components such as FPGA (field-programmable gate array) or ASIC, and the '~unit' performs certain roles. However, '~part' is not limited to software or hardware. The '~ part' may be configured to reside in an addressable storage medium and may be configured to reproduce on one or more processors. Therefore, as an example, '~ part' refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, and procedures. , subroutines, segments of program channels, drivers, firmware, microchannels, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and 'parts' may be combined into a smaller number of components and 'parts' or may be further separated into additional components and 'parts'. In addition, the components and 'parts' may be implemented to regenerate one or more CPUs within the device or secure multimedia card.

Although the present invention has been described above with reference to preferred embodiments, those skilled in the art may make various modifications and changes to the present invention without departing from the spirit and scope of the present invention as set forth in the claims below. You will understand that you can do it.

100: imaging device
111: image sensor
112: data processing unit
113: control unit
114: Department of Communications
115: display unit
116: User interface unit
117: encoding unit
118: database
119: Light source unit
120: Pan tilt unit

Claims (15)

An image sensor that captures images and generates image data;
a data processing unit that detects an object using the image data, determines a head portion of the object, and generates distribution data for the head portion; and
It includes a communication unit that transmits the image data and the distribution data to a management server,
The data processing unit divides the image data into a plurality of zones, detects the color of a position corresponding to the head portion on the image data divided into a plurality of zones using the distribution data, and detects the color of the position corresponding to the head portion for each of the plurality of zones. An imaging device that generates regional color data for the head unit by calculating the average or sum of colors.
According to paragraph 1,
The distribution data includes at least one of a heat map and coordinate information for the head portion.
delete delete According to paragraph 1,
The communication unit transmits at least one of the color data and the color data for each zone to the management server.
According to paragraph 1,
An imaging device further comprising a control unit that determines a health state of poultry using at least one of the color data and the color data for each zone.
According to clause 6,
The control unit determines the health status of the poultry by comparing the color data with a preset threshold range.
An image sensor that captures images and generates image data; a data processing unit that detects an object using the image data, determines a head portion of the object, and generates distribution data for the head portion; and an imaging device including a communication unit that transmits the image data and the distribution data; and
A management server that receives the image data and the distribution data, detects the color of the position corresponding to the head in the image data using the distribution data, and analyzes the detected color to determine the health status of the poultry.
Includes,
The data processing unit divides the image data into a plurality of zones, detects the color of a position corresponding to the head portion on the image data divided into a plurality of zones using the distribution data, and detects the color of the position corresponding to the head portion for each of the plurality of zones. A poultry management system that generates color data by zone for the head by calculating the average or sum of colors.
According to clause 8,
The distribution data is a poultry management system including at least one of a heat map and coordinate information for the head portion.
delete delete According to clause 8,
A poultry management system wherein the communication unit transmits at least one of the color data and the color data for each zone to the management server.
According to clause 8,
A poultry management system in which the management server determines the health status of poultry using at least one of the color data and the color data for each zone.
According to clause 13,
A poultry management system in which the management server determines the health status of the poultry by comparing the color data with a preset critical range.
Generating image data by taking an image;
detecting an object from the image data;
determining the head portion of the object;
generating distribution data for the head portion;
detecting the color of a position corresponding to the head portion on the image data using the distribution data; and
It includes a step of determining the health status of the poultry by analyzing the detected color,
The step of detecting the color of a position corresponding to the head portion on the image data using the distribution data includes:
Dividing the image data into a plurality of zones, detecting the color of a position corresponding to the head portion on the image data divided into a plurality of zones using the distribution data, and determining the color of the head portion for each of the plurality of zones. A poultry management method comprising generating regional color data for the head portion by calculating an average value or total.

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