Image monitoring method for monitoring in incubation process of farm

Abstract

The invention discloses an image monitoring method for monitoring in the incubation process of a farm. The image monitoring method comprises the following steps: acquiring an image of a monitored object by a monitoring terminal; determining, by the monitoring terminal, a type of a defect of the monitored object based on the image of the monitored object; if the monitoring terminal determines that the defect of the monitored object is the first type of defect, the monitoring terminal determines to perform first type division on the image of the monitored object; if the monitoring terminal determines that the defect of the monitored object is a second type of defect, the monitoring terminal determines to carry out second type division on the image of the monitored object; the monitoring terminal performs first-class division or second-class division on the image of the monitoring object; and the monitoring terminal sends the image of the monitored object subjected to the first class division or the second class division to the user terminal.

Description

Technical Field

[0001] This invention relates to the field of image communication technology, and in particular to an image monitoring method for monitoring during the hatching process in a breeding farm. Background Technology

[0002] Currently, poultry farms have implemented remote management. This remote management reduces labor costs and the likelihood of poultry contracting infectious diseases. Furthermore, because it eliminates the need for frequent entry and exit of farm staff into the poultry living areas, remote management prevents the spread of zoonotic diseases, greatly ensuring the safety of farm workers. For example, at a broiler farm we surveyed, the farm has implemented a system where staff can remotely monitor egg development through the use of monitoring terminals and user terminals. However, this system still has some limitations. Summary of the Invention

[0003] This invention provides an image monitoring method for monitoring during the hatching process in a breeding farm. The method includes: acquiring an image of a monitored object by a monitoring terminal; determining the type of defect of the monitored object based on the image of the monitored object by the monitoring terminal; if the monitoring terminal determines that the defect of the monitored object is a first type of defect, then the monitoring terminal determines to classify the image of the monitored object into a first type; if the monitoring terminal determines that the defect of the monitored object is a second type of defect, then the monitoring terminal determines to classify the image of the monitored object into a second type, wherein the first type of classification is different from the second type of classification; the monitoring terminal classifies the image of the monitored object into either the first type or the second type; and the monitoring terminal sends the image of the monitored object that has been classified into either the first type or the second type to a user terminal.

[0004] In a preferred embodiment, the first classification of the image of the monitored object by the monitoring terminal includes the following steps: the monitoring terminal determines the defect area of ​​the image of the monitored object; the monitoring terminal determines the surrounding area of ​​the defect area based on the defect area; and the monitoring terminal determines the outer perimeter area of ​​the defect area based on the surrounding area of ​​the defect area.

[0005] In a preferred embodiment, the process of a monitoring terminal sending an image of a monitored object classified into a first category to a user terminal includes the following steps: the monitoring terminal generates a first image to be sent, wherein the first image to be sent has a first region, a second region, and a third region, wherein the first region is an image of a defective region with a first resolution, wherein the second region is an image of the surrounding region of the defective region with a second resolution, and wherein the third region is an image of the outer region of the defective region with a third resolution, wherein the first resolution is higher than the second resolution, and the second resolution is higher than the third resolution, wherein the second region and the third region are configured to be selectable by the monitoring terminal, and the second region has a number representing the first image to be sent and a number representing the second region, and the third region has a number representing the first image to be sent and a number representing the third region; the monitoring terminal then sends the first image to be sent to the user terminal.

[0006] In a preferred embodiment, the second classification of the image of the monitored object by the monitoring terminal includes the following steps: the monitoring terminal determines the contour region of the image of the monitored object; the monitoring terminal determines the surrounding region of the contour region based on the contour region; and the monitoring terminal determines the outer perimeter region of the contour region based on the surrounding region of the contour region.

[0007] In a preferred embodiment, the process of a monitoring terminal sending an image of a monitored object classified into a second category to a user terminal includes the following steps: the monitoring terminal generates a second image to be sent, wherein the second image to be sent has a fourth region, a fifth region, and a sixth region, wherein the fourth region is an image of a contour region with a first resolution, the fifth region is an image of the surrounding region of the contour region with a second resolution, and the sixth region is an image of the outer region of the contour region with a third resolution; the fifth and sixth regions are configured to be selectable by the monitoring terminal, and the fifth region has a number representing the second image to be sent and a number representing the fifth region, and the sixth region has a number representing the second image to be sent and a number representing the sixth region; the monitoring terminal then sends the second image to be sent to the user terminal.

[0008] In a preferred embodiment, the method further includes: receiving a first image to be sent by a user terminal; determining whether a clearer image of the surrounding area of ​​the defective region is needed by the user; if the user determines that a clearer image of the surrounding area of ​​the defective region is needed, then the user selects a second area of ​​the first image to be sent; after the user selects the second area of ​​the first image to be sent, the user terminal sends the number of the first image to be sent and the number of the second area to a monitoring terminal; after the monitoring terminal receives the number of the first image to be sent and the number of the second area sent by the user terminal, the monitoring terminal sends an image of the surrounding area of ​​the defective region with a first resolution to the user terminal; determining whether a clearer image of the outer area of ​​the defective region is needed by the user; if the user determines that a clearer image of the outer area of ​​the defective region is needed, then the user selects a third area of ​​the first image to be sent; after the user selects the third area of ​​the first image to be sent, the user terminal sends the number of the first image to be sent and the number of the third area to the monitoring terminal; after the monitoring terminal receives the number of the first image to be sent and the number of the third area sent by the user terminal, the monitoring terminal sends an image of the outer area of ​​the defective region with a first resolution to the user terminal.

[0009] The present invention also provides an image monitoring system for monitoring during the hatching process in a breeding farm. The system includes units for performing the following operations: acquiring an image of the monitored object by a monitoring terminal; determining the type of defect of the monitored object based on the image of the monitored object by the monitoring terminal; if the monitoring terminal determines that the defect of the monitored object is a first type defect, then the monitoring terminal determines to classify the image of the monitored object into a first type; if the monitoring terminal determines that the defect of the monitored object is a second type defect, then the monitoring terminal determines to classify the image of the monitored object into a second type, wherein the first type classification is different from the second type classification; classifying the image of the monitored object into the first type or the second type by the monitoring terminal; and sending the image of the monitored object classified into the first type or the second type by the monitoring terminal to a user terminal.

[0010] In a preferred embodiment, the first classification of the image of the monitored object by the monitoring terminal includes the following steps: the monitoring terminal determines the defect area of ​​the image of the monitored object; the monitoring terminal determines the surrounding area of ​​the defect area based on the defect area; and the monitoring terminal determines the outer perimeter area of ​​the defect area based on the surrounding area of ​​the defect area.

[0011] In a preferred embodiment, the process of a monitoring terminal sending an image of a monitored object classified into a first category to a user terminal includes the following steps: the monitoring terminal generates a first image to be sent, wherein the first image to be sent has a first region, a second region, and a third region, wherein the first region is an image of a defective region with a first resolution, wherein the second region is an image of the surrounding region of the defective region with a second resolution, and wherein the third region is an image of the outer region of the defective region with a third resolution, wherein the first resolution is higher than the second resolution, and the second resolution is higher than the third resolution, wherein the second region and the third region are configured to be selectable by the monitoring terminal, and the second region has a number representing the first image to be sent and a number representing the second region, and the third region has a number representing the first image to be sent and a number representing the third region; the monitoring terminal then sends the first image to be sent to the user terminal.

[0012] In a preferred embodiment, the second classification of the image of the monitored object by the monitoring terminal includes the following steps: the monitoring terminal determines the contour region of the image of the monitored object; the monitoring terminal determines the surrounding region of the contour region based on the contour region; and the monitoring terminal determines the outer perimeter region of the contour region based on the surrounding region of the contour region.

[0013] Compared with the prior art, the present invention has the following advantages:

[0014] The slow image reading speed on the user terminal side is mainly due to two factors: slow network transmission speed and limited image processing capabilities of the user terminal. A common approach to address this is to add more modems and Wi-Fi routers, while simultaneously upgrading the user terminal configuration. However, this method significantly increases the cost of the farm, and deploying too many wireless routers within a given area can easily lead to signal interference. To solve these existing technical problems, this invention proposes a novel image monitoring method for monitoring the hatching process in aquaculture farms. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the system architecture of one embodiment of the present invention.

[0016] Figure 2 This is a flowchart of a method according to an embodiment of the present invention.

[0017] Figure 3 This is a schematic diagram of the images of the monitoring objects classified into the first category according to the present invention.

[0018] Figure 4 This is a schematic diagram of the first image to be sent according to the present invention.

[0019] Figure 5This is a schematic diagram of the images of the monitoring objects classified into the second category according to the present invention. Detailed Implementation

[0020] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings, but it should be understood that the scope of protection of the present invention is not limited to the specific embodiments.

[0021] As mentioned earlier, a farm we surveyed has implemented a system where staff can remotely monitor egg development using a combination of monitoring and user terminals. However, staff have reported a common problem with the system: slow image reading speed on the user terminal side, which often leads to overtime work. Our research revealed two main reasons for this slow image reading speed: First, the network transmission speed is slow. To ensure accurate egg assessment, the system currently sends images to the user terminal at high resolution. With limited network bandwidth, transmitting a large number of high-resolution images inevitably reduces transmission speed. Second, the user terminal's image processing capabilities are limited. To save costs, the farm typically provides integrated graphics cards for its user terminals, limiting their image processing capabilities, especially when dealing with a large number of images, which prolongs the processing and display time. To address the aforementioned problems, the general approach is to add more modems and Wi-Fi routers, while simultaneously upgrading user terminal configurations. However, this method significantly increases the cost of the farm (currently, the annual rental price for a 500M bandwidth fiber optic cable is generally over 1,000 yuan, and a suitable dedicated graphics card also costs around 1,000 yuan). Furthermore, deploying too many wireless routers within a certain area can easily lead to signal interference. To solve the practical problems existing in this farm, this invention proposes a novel image monitoring method for monitoring the hatching process in the farm.

[0022] Figure 1 This is a schematic diagram of the system architecture of one embodiment of the present invention. As shown in the figure, the system of the present invention includes multiple user terminals, which can be known desktop computers; the incubator can have one or more monitoring terminals ( Figure 1 The example shown is of a monitoring terminal only. The monitoring terminal can be a self-propelled robot with certain computing capabilities. The monitoring terminal can move near the object being monitored (e.g., an egg) to capture images. Figure 1 The image shows a hatchery with multiple oval-shaped eggs, which is understandable. Figure 1 This is just an illustration; the actual number of eggs in a hatchery would be much greater.

[0023] Example 1

[0024] Figure 2 This is a flowchart of a method according to an embodiment of the present invention. As shown in the figure, the method of the present invention includes the following steps:

[0025] Step 1: The monitoring terminal acquires an image of the monitored object; in one example, acquiring an image of the monitored object means taking a picture of the monitored object. For the sake of convenience, the following text will use an egg as the monitored object.

[0026] Step 2: The monitoring terminal determines the type of defect in the monitored object based on the image. During the research, we found that farms requesting this service generally categorize egg defects into surface damage, irregular shape, and excessively large or small size (eggs ultimately deemed defective no longer need to be removed from the hatchery for harmless disposal). Our research revealed that, to accelerate image transmission, the method for processing images of eggs with surface damage and irregular shapes should differ from the method for processing images of excessively large or small eggs. Therefore, the monitoring terminal needs to make a preliminary judgment on the type of egg defect. It is understandable that the monitoring terminal can use known machine learning methods (e.g., supervised learning) to determine the type of defect. The monitoring terminal uses a training process to acquire the ability to identify the types of defects in eggs. Simultaneously, through known machine learning and image processing techniques, the monitoring terminal can also locate the defects in the eggs. The use of machine learning to give machines machine vision capabilities is a well-known technology in the field and does not involve the inventive concept of this invention, so it will not be elaborated upon here. Furthermore, the monitoring terminal only makes a preliminary judgment on whether the eggs are defective; the final decision on whether the eggs are truly defective rests with the user (in this article, the user refers to the farm staff, and the user terminal is operated by the user). The monitoring terminal only sends eggs that are preliminarily determined to be defective to the user terminal to save transmission resources.

[0027] Step 3: If the monitoring terminal determines that the defect of the monitored object is a first-class defect, the monitoring terminal determines to classify the image of the monitored object into the first class; in this invention, the first-class defect mainly refers to surface damage and irregular shape defects;

[0028] Step 4: If the monitoring terminal determines that the defect of the monitored object is a second type of defect, then the monitoring terminal determines to classify the image of the monitored object into a second type. The first type of classification is different from the second type of classification. In this invention, the first type of defect mainly refers to the defect of eggs that are too big or too small.

[0029] Step 5: The monitoring terminal classifies the image of the monitored object into either the first or second category;

[0030] Step 6: The monitoring terminal sends the images of the monitored objects that have been classified into the first or second categories to the user terminal.

[0031] Example 2

[0032] The following combination Figure 3 Example 2 is introduced. In Example 2, the first category classification of the image of the monitored object by the monitoring terminal includes the following steps:

[0033] The monitoring terminal determines the defect area in the image of the monitored object; Figure 3 In the diagram, the smallest elliptical region is the defect region, where the first type of defect is located. This first type of defect can be surface damage or irregular shape. It is understood that the defect region itself should be slightly larger than the defect, because determining whether a defect affects hatching requires understanding both the defect itself and its immediate surroundings. Furthermore, judging a defect also requires the light contrast of the defect itself and its immediate surroundings as a reference. If only the defect itself is defined as the defect region, the image of the defect itself may lack the contrast of brightness and darkness of its immediate surroundings, making it impossible for users to determine whether the defect affects hatching based on the image. Of course, the extent of the defect region should not be too large, otherwise the purpose of this invention cannot be achieved. For reference, based on our research of hatcheries, a defect region area that is generally about 20% larger than the defect area is appropriate.

[0034] The monitoring terminal determines the surrounding area of ​​the defect area based on the defect area; in Figure 3 In the diagram, the area defined by the square shaded area is the surrounding area of ​​the defect area (it is necessary to distinguish between the surrounding area of ​​the defect area and the area immediately adjacent to the defect); the surrounding area of ​​this defect area is less relevant to determining whether the defect affects hatching than the defect itself and the area immediately adjacent to the defect, so it is defined as the surrounding area of ​​the defect area.

[0035] The monitoring terminal determines the outer area of ​​the defect area based on the surrounding area of ​​the defect area. Figure 3 In this context, the area excluding the defective area and the area surrounding the defective area is the outer area of ​​the defective area. This area has the lowest reference value for judging whether the defect affects hatching and generally does not affect the judgment result of whether the defect affects hatching.

[0036] The process of the monitoring terminal sending images of the monitored objects classified into the first category to the user terminal includes the following steps:

[0037] A first image to be transmitted is generated by a monitoring terminal. This first image has a first region, a second region, and a third region. The first region is an image of the defect area with a first resolution. The second region is an image of the surrounding area of ​​the defect area with a second resolution. The third region is an image of the outermost area of ​​the defect area with a third resolution. The first resolution is higher than the second resolution, and the second resolution is higher than the third resolution. The second and third regions are configurable as selectable by the monitoring terminal. The second region has a number representing the first image to be transmitted and a second region number, and the third region has a number representing the first image to be transmitted and a third region number. Figure 4 Understandable Figure 4 In fact, it is with Figure 3 The same image, the difference between the two is that... Figure 3 The central defect area, the surrounding area of ​​the defect area, and the outermost area of ​​the defect area all have the same resolution. Figure 3 It simply divides a single frame of an egg image into the defect area, the area surrounding the defect area, and the outermost area of ​​the defect area, while... Figure 4 It is equivalent to Figure 3 Preservation of resolution in defective areas Figure 3 The resolution of the area surrounding the defective region was reduced, and... Figure 3 The resolution of the outer region of the defective area is set even lower. In one example, the first resolution could be 1080P, the second 720P, and the third 240P. During the research, it was found that although the outer region of the defective area is far less important than the defective area itself in determining whether the egg can continue to hatch, directly lowering the resolution of the outer region of the defective area to, for example, 240P, could reduce the overall viewing quality of the image due to the drastic change in resolution. This could negatively impact the user's judgment on whether the egg can continue to hatch. Furthermore, the outer region of the defective area adjacent to it also plays a role in determining whether the egg can continue to hatch. Therefore, it was ultimately decided to use three resolutions to generate the images to be sent. In addition, since the monitoring terminal relies entirely on machine vision to determine whether the egg has a defect, transmitting the outer region of the defective area is still meaningful, as it helps users discover defects that the monitoring terminal might miss.

[0038] The monitoring terminal sends the first image to be sent to the user terminal.

[0039] Example 3

[0040] The following combination Figure 5 Example 3 will be introduced. In Example 3, the second classification of the image of the monitored object by the monitoring terminal includes the following steps:

[0041] The monitoring terminal determines the outline region of the image of the monitored object; such as Figure 3 As shown, since the second type of defect is that the egg is too large or too small, the determination of whether the second type of defect affects the hatching of the egg is mainly based on the user's judgment of the egg outline. The monitoring terminal can, for example, determine the eggshell through a known edge detection algorithm, and then expand the elliptical range based on the eggshell to obtain the outer boundary of the outline region, and shrink the elliptical range based on the eggshell to obtain the inner boundary of the outline region. The area between the outer boundary and the inner boundary of the outline region is the outline region. Based on our research, if only the egg outline (i.e., the eggshell) is sent to the user, the image will be severely distorted due to the lack of necessary light changes, which is not conducive to determining whether the hatching of the egg should be stopped. Using the outline region instead of the egg outline itself can improve the image realism.

[0042] The monitoring terminal determines the surrounding area of ​​the contour region based on the contour region; the area between the inner boundary of the contour region and the inner boundary of the contour region is the surrounding area of ​​the contour region. Delineating this part of the area is mainly to prevent image distortion.

[0043] The monitoring terminal determines the outer region of the contour region based on the surrounding area of ​​the contour region; the area other than the aforementioned contour region and the surrounding area of ​​the contour region is the outer region of the contour region.

[0044] The process of the monitoring terminal sending images of the monitored objects, which have been classified into the second category, to the user terminal includes the following steps:

[0045] The monitoring terminal generates a second image to be transmitted, which has a fourth region, a fifth region, and a sixth region. The fourth region is an image of a contour region with a first resolution, the fifth region is an image of the surrounding region of the contour region with a second resolution, and the sixth region is an image of the outer region of the contour region with a third resolution. The fifth and sixth regions are configured to be selectable by the monitoring terminal, and the fifth region has the number of the second image to be transmitted and the number of the fifth region, and the sixth region has the number of the second image to be transmitted and the number of the sixth region. The definition of the second image to be transmitted, the definition of the regions, and the resolution size have been introduced previously and will not be repeated here. The fifth and sixth regions being configured to be selectable by the monitoring terminal means that the monitoring terminal sets the fifth and sixth regions to be clickable. This can be achieved by specifying the fifth and sixth regions as clickable when encoding the image data. After the user terminal receives the image data, based on the fact that the fifth and sixth regions are specified as clickable, it implements the fifth and sixth regions as clickable through the built-in software.

[0046] The monitoring terminal sends a second image to the user terminal. Our internal testing shows that using the method of this invention can improve image display speed (which depends on the image transmission speed and the speed at which the user terminal processes and displays image data) by an average of approximately 20%.

[0047] Example 4

[0048] In Embodiment 4, the method of the present invention further includes: receiving a first image to be sent by a user terminal; determining whether a clearer image of the surrounding area of ​​the defective area is needed by the user; in one example, the user may think that there is less information in the defective area and that a clearer image of the surrounding area of ​​the defective area is needed to determine whether the egg needs to be stopped from hatching, so there may be a situation where a clearer image of the surrounding area of ​​the defective area is needed; the solution of Embodiment 4 realizes the interaction between the user and the monitoring terminal, improving the flexibility of the system; if the user determines that a clearer image of the surrounding area of ​​the defective area is needed, the user selects a second area of ​​the first image to be sent; in one example, the user can move the mouse to the second area and then click the mouse to complete the operation of the user selecting the second area of ​​the first image to be sent; after the user selects the second area of ​​the first image to be sent, the user terminal sends the number of the first image to be sent and the number of the second area to the monitoring terminal; After receiving the number of the first image to be sent and the number of the second region from the user terminal, the monitoring terminal sends an image of the surrounding area of ​​the defect area with a first resolution to the user terminal. The user then determines whether a clearer image of the surrounding area of ​​the defect area is needed. In one example, the user may discover a new defect that the monitoring terminal missed detecting in the surrounding area of ​​the defect area, thus requiring a clearer image of the surrounding area. If the user determines that a clearer image of the surrounding area of ​​the defect area is needed, the user selects the third region of the first image to be sent. After the user selects the third region of the first image to be sent, the user terminal sends the number of the first image to be sent and the number of the third region to the monitoring terminal. After receiving the number of the first image to be sent and the number of the third region from the user terminal, the monitoring terminal sends an image of the surrounding area of ​​the defect area with a first resolution to the user terminal.

[0049] Example 5

[0050] The present invention also provides an image monitoring system for monitoring during the hatching process in a breeding farm. The system includes units for performing the following operations: acquiring an image of the monitored object by a monitoring terminal; determining the type of defect of the monitored object based on the image of the monitored object by the monitoring terminal; if the monitoring terminal determines that the defect of the monitored object is a first type defect, then the monitoring terminal determines to classify the image of the monitored object into a first type; if the monitoring terminal determines that the defect of the monitored object is a second type defect, then the monitoring terminal determines to classify the image of the monitored object into a second type, wherein the first type classification is different from the second type classification; classifying the image of the monitored object into the first type or the second type by the monitoring terminal; and sending the image of the monitored object classified into the first type or the second type by the monitoring terminal to a user terminal.

[0051] Furthermore, the first-class classification of the image of the monitored object by the monitoring terminal includes the following steps: the monitoring terminal determines the defect area of ​​the image of the monitored object; the monitoring terminal determines the surrounding area of ​​the defect area based on the defect area; and the monitoring terminal determines the outer area of ​​the defect area based on the surrounding area of ​​the defect area.

[0052] Furthermore, the process of the monitoring terminal sending images of the monitored objects classified in the first category to the user terminal includes the following steps: the monitoring terminal generates a first image to be sent, wherein the first image to be sent has a first region, a second region, and a third region, wherein the first region is an image of the defect region with a first resolution, wherein the second region is an image of the surrounding region of the defect region with a second resolution, and wherein the third region is an image of the outer region of the defect region with a third resolution, wherein the first resolution is higher than the second resolution, and the second resolution is higher than the third resolution, wherein the second region and the third region are configured to be selectable by the monitoring terminal, and the second region has a number of the first image to be sent and a number of the second region, and the third region has a number of the first image to be sent and a number of the third region; the monitoring terminal sends the first image to be sent to the user terminal.

[0053] Furthermore, the second classification of the image of the monitored object by the monitoring terminal includes the following steps: the monitoring terminal determines the contour region of the image of the monitored object; the monitoring terminal determines the surrounding region of the contour region based on the contour region; and the monitoring terminal determines the outer region of the contour region based on the surrounding region of the contour region.

[0054] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries.

Claims

1. An image monitoring method for monitoring during the hatching process in a breeding farm, characterized in that, The method includes: Images of the monitored objects are acquired by the monitoring terminal; The monitoring terminal determines the type of defect in the monitored object based on an image of the monitored object; If the monitoring terminal determines that the defect of the monitored object is a first-class defect, then the monitoring terminal determines to classify the image of the monitored object into the first class. If the monitoring terminal determines that the defect of the monitored object is a second type of defect, then the monitoring terminal determines to classify the image of the monitored object into a second type, wherein the first type of classification is different from the second type of classification; The monitoring terminal classifies the image of the monitored object into a first category or a second category. The monitoring terminal sends images of the monitored objects that have been classified into the first or second categories to the user terminal.

2. The method according to claim 1, wherein, The first category classification of the image of the monitored object by the monitoring terminal includes the following steps: The monitoring terminal determines the defect area in the image of the monitored object; The monitoring terminal determines the surrounding area of ​​the defect area based on the defect area. The monitoring terminal determines the outer area of ​​the defect area based on the surrounding area of ​​the defect area.

3. The method according to claim 2, wherein, The process of the monitoring terminal sending images of the monitored objects classified into the first category to the user terminal includes the following steps: A first image to be sent is generated by a monitoring terminal. The first image to be sent has a first region, a second region, and a third region. The first region is an image of the defective region with a first resolution. The second region is an image of the surrounding region of the defective region with a second resolution. The third region is an image of the outer region of the defective region with a third resolution. The first resolution is higher than the second resolution, and the second resolution is higher than the third resolution. The second region and the third region are configured to be selectable by the monitoring terminal. The second region has the number of the first image to be sent and the number of the second region. The third region has the number of the first image to be sent and the number of the third region. The monitoring terminal sends the first image to be sent to the user terminal.

4. The method according to claim 3, wherein, The second classification of the image of the monitored object by the monitoring terminal includes the following steps: The monitoring terminal determines the outline region of the image of the monitored object; The monitoring terminal determines the surrounding area of ​​the contour region based on the contour region; The monitoring terminal determines the outer region of the contour region based on the surrounding region of the contour region.

5. The method according to claim 4, wherein, The process of the monitoring terminal sending images of the monitored objects, which have been classified into the second category, to the user terminal includes the following steps: A second image to be transmitted is generated by the monitoring terminal. The second image to be transmitted has a fourth region, a fifth region, and a sixth region. The fourth region is an image of the contour region with a first resolution. The fifth region is an image of the surrounding region of the contour region with a second resolution. The sixth region is an image of the outer region of the contour region with a third resolution. The fifth region and the sixth region are configured to be selectable by the monitoring terminal. The fifth region has the number of the second image to be transmitted and the number of the fifth region. The sixth region has the number of the second image to be transmitted and the number of the sixth region. The monitoring terminal sends the second image to be sent to the user terminal.

6. The method according to claim 5, wherein, The method further includes: The first image to be sent is received by the user terminal; The user determines whether a clearer image of the area surrounding the defective region is needed. If the user determines that a clearer image of the surrounding area of ​​the defective area is needed, the user selects the second area of ​​the first image to be sent; After the user selects the second region of the first image to be sent, the user terminal sends the number of the first image to be sent and the number of the second region to the monitoring terminal. After the monitoring terminal receives the number of the first image to be sent and the number of the second region sent by the user terminal, the monitoring terminal sends an image of the surrounding area of ​​the defect area with a first resolution to the user terminal. The user determines whether a clearer image of the area surrounding the defective region is needed. If the user determines that a clearer image of the outer area of ​​the defective area is needed, the user selects the third area of ​​the first image to be sent; After the user selects the third region of the first image to be sent, the user terminal sends the number of the first image to be sent and the number of the third region to the monitoring terminal. After the monitoring terminal receives the number of the first image to be sent and the number of the third region sent by the user terminal, the monitoring terminal sends an image of the outer region of the defect region with a first resolution to the user terminal.

7. An image monitoring system for monitoring the hatching process in a breeding farm, characterized in that, The system includes units for performing the following operations: Images of the monitored objects are acquired by the monitoring terminal; The monitoring terminal determines the type of defect in the monitored object based on an image of the monitored object; If the monitoring terminal determines that the defect of the monitored object is a first-class defect, then the monitoring terminal determines to classify the image of the monitored object into the first class. If the monitoring terminal determines that the defect of the monitored object is a second type of defect, then the monitoring terminal determines to classify the image of the monitored object into a second type, wherein the first type of classification is different from the second type of classification; The monitoring terminal classifies the image of the monitored object into a first category or a second category. The monitoring terminal sends images of the monitored objects that have been classified into the first or second categories to the user terminal.

8. The system according to claim 7, wherein, The first category classification of the image of the monitored object by the monitoring terminal includes the following steps: The monitoring terminal determines the defect area in the image of the monitored object; The monitoring terminal determines the surrounding area of ​​the defect area based on the defect area. The monitoring terminal determines the outer area of ​​the defect area based on the surrounding area of ​​the defect area.

9. The system according to claim 8, wherein, The process of the monitoring terminal sending images of the monitored objects classified into the first category to the user terminal includes the following steps: A first image to be sent is generated by a monitoring terminal. The first image to be sent has a first region, a second region, and a third region. The first region is an image of the defective region with a first resolution. The second region is an image of the surrounding region of the defective region with a second resolution. The third region is an image of the outer region of the defective region with a third resolution. The first resolution is higher than the second resolution, and the second resolution is higher than the third resolution. The second region and the third region are configured to be selectable by the monitoring terminal. The second region has the number of the first image to be sent and the number of the second region. The third region has the number of the first image to be sent and the number of the third region. The monitoring terminal sends the first image to be sent to the user terminal.

10. The system according to claim 9, wherein, The second classification of the image of the monitored object by the monitoring terminal includes the following steps: The monitoring terminal determines the outline region of the image of the monitored object; The monitoring terminal determines the surrounding area of ​​the contour region based on the contour region; the monitoring terminal determines the outer perimeter area of ​​the contour region based on the surrounding area of ​​the contour region.

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