Automatic Behavior Monitoring Method, Device and Electronic Equipment for Caged Laying Hens

Abstract

The present invention provides a method, device and electronic device for automatically monitoring the behaviors of caged laying hens. The method includes: acquiring video data of the laying hen breeding scenario, where the body of each laying hen in the laying hen breeding scenario is located inside the cage; inputting at least one consecutive video frame image in the video data into a behavior analysis model, and obtaining the total feeding duration of the laying hens in each cage position, the number of eggs falling into the egg collecting belt area in each cage position, the number of eggs not falling into the egg collecting belt area and their positions output by the behavior analysis model. This method can improve the automation degree and monitoring accuracy of the behavior monitoring of caged laying hens.

Description

Technical Field

[0001] The present invention relates to the field of computer vision technology, and particularly to a method, device and electronic device for automatically monitoring the behaviors of caged laying hens. Background Art

[0002] In the research on the intelligent breeding technology of poultry in related technologies, domestic and foreign scholars mainly focus on the research and development of intelligent feeding equipment. For example, automatic disinfection robots and feeding management robots have been developed for free-range broilers; there is also research on inspection robots based on multi-layer caged laying hen houses, but most of them only continuously monitor the living environment parameters of chickens through multiple sensors; there is also research using thermal imagers to detect temperature anomalies or immobile chickens. However, for the monitoring of behaviors such as feeding and egg laying of caged chickens, there is currently only a small amount of laboratory behavior classification research, and there is a large gap from actual production conditions, with low automation and low monitoring accuracy. Summary of the Invention

[0003] The present invention provides a method, device and electronic device for automatically monitoring the behaviors of caged laying hens, which can improve the automation degree and monitoring accuracy of the behavior monitoring of caged laying hens.

[0004] The present invention provides a method for automatically monitoring the behaviors of caged laying hens, including:

[0005] Obtaining video data of the laying hen breeding scenario, where the body of each laying hen in the laying hen breeding scenario is located inside the cage;

[0006] Inputting at least one consecutive video frame image in the video data into a behavior analysis model, and obtaining the total feeding duration of the laying hens in each cage position, the number of eggs falling into the egg collection belt area and the number and positions of eggs not falling into the egg collection belt area output by the behavior analysis model;

[0007] Wherein, the behavior analysis model is built based on an object detection framework, and includes a feature extraction network, a feature aggregation network, a detection head and two segmentation heads. The behavior analysis model is used for:

[0008] Successively performing feature extraction and feature fusion on at least one consecutive video frame image in the video data;

[0009] Based on the fused features, using the one detection head to detect and identify the chicken body, chicken head and eggs, obtaining the position information of the chicken body, chicken head and eggs corresponding to the at least one video frame image respectively, and using the two segmentation heads to segment the feeding trough area and the egg collection belt area respectively, obtaining the position information of the feeding trough area and the egg collection belt area;

[0010] Based on the position information of the chicken body, chicken head, eggs, feeding trough area, and egg collection belt area corresponding to each of the at least one video frame, determine the positional relationship between the chicken head and the feeding trough area, the positional matching relationship between the chicken head and the chicken body, and the positional relationship between the eggs and the egg collection belt area corresponding to each of the at least one video frame;

[0011] Based on the positional relationship between the chicken head and the feeding trough area and the positional matching relationship between the chicken head and the chicken body corresponding to each of the at least one video frame, determine the total feeding duration of the laying hens in each cage position. Based on the positional relationship between the eggs and the egg collection belt area corresponding to each of the at least one video frame, determine the number of eggs that have fallen into the egg collection belt area and the number and positions of the eggs that have not fallen into the egg collection belt area in each cage position.

[0012] According to the method for automatically monitoring the behavior of caged laying hens provided by the present invention, after respectively performing feature extraction and feature fusion on at least one consecutive video frame in the video data, three different-scale fusion features are obtained;

[0013] Based on the fused features, use the one detection head to detect and identify the chicken body, chicken head, and eggs, and obtain the position information of the chicken body, chicken head, and eggs corresponding to each of the at least one video frame, including:

[0014] Based on the three different-scale fusion features, use the one detection head to detect and identify the chicken body, chicken head, and eggs, and obtain the position information of the chicken body, chicken head, and eggs corresponding to each of the at least one video frame.

[0015] According to the method for automatically monitoring the behavior of caged laying hens provided by the present invention, use the two segmentation heads to respectively segment the feeding trough area and the egg collection belt area, and obtain the position information of the feeding trough area and the egg collection belt area, including:

[0016] Based on the fusion feature with the highest resolution among the three different-scale fusion features, use the two segmentation heads to respectively segment the feeding trough area and the egg collection belt area, and obtain the position information of the feeding trough area and the egg collection belt area.

[0017] According to the method for automatically monitoring the behavior of caged laying hens provided by the present invention, based on the position information of the chicken body, chicken head, eggs, feeding trough area, and egg collection belt area corresponding to each of the at least one video frame, determine the positional matching relationship between the chicken head and the chicken body, including:

[0018] Calculate the intersection over union between the chicken body and the chicken head corresponding to each of the at least one video frame;

[0019] Based on the intersection over union, the Hungarian algorithm is used to match the chicken heads with the chicken bodies to determine the position matching relationship between the chicken heads and the chicken bodies.

[0020] According to the method for automatically monitoring the behaviors of caged laying hens provided by the present invention, based on the position relationship between the chicken heads and the feeding trough areas respectively corresponding to the at least one video frame image and the position matching relationship between the chicken heads and the chicken bodies, determining the total feeding duration of the laying hens in each cage position includes:

[0021] Regarding the chicken heads whose positions in the at least one video frame image are within the feeding trough area as the feeding chicken heads;

[0022] Based on the position matching relationship between the feeding chicken heads and the chicken bodies, determining the positions of the chicken bodies corresponding to the feeding chicken heads and the cage positions to which they belong;

[0023] Based on the duration of the at least one video frame image, the feeding chicken heads in the at least one video frame image, and the positions of the chicken bodies corresponding to the feeding chicken heads and the cage positions to which they belong, determining the total feeding duration of the laying hens in each cage position.

[0024] According to the method for automatically monitoring the behaviors of caged laying hens provided by the present invention, based on the position relationship between the eggs and the egg collecting belt areas respectively corresponding to the at least one video frame image, determining the number of eggs that have fallen into the egg collecting belt area and the number and positions of the eggs that have not fallen into the egg collecting belt area in each cage position includes:

[0025] Dividing the egg collecting belt area according to the boundaries between the cage positions;

[0026] Based on the number of eggs located in the egg collecting belt areas corresponding to each cage position in one or more video frame images with the latest time in the at least one video frame image, determining the number of eggs that have fallen into the egg collecting belt area in each cage position, and based on the number and positions of the eggs located outside the egg collecting belt area in one or more video frame images with the latest time in the at least one video frame image, determining the number and positions of the eggs that have not fallen into the egg collecting belt area.

[0027] The present invention also provides an apparatus for automatically monitoring the behaviors of caged laying hens, including:

[0028] An acquisition module, configured to acquire video data of a laying hen breeding scenario, where the body of each laying hen in the laying hen breeding scenario is located inside a cage;

[0029] An input module, configured to acquire video data of a laying hen breeding scenario, where the body of each laying hen in the laying hen breeding scenario is located inside a cage;

[0030] Input at least one consecutive video frame in the video data into the behavior analysis model, and obtain the total feeding duration of laying hens in each cage position, the number of eggs that fall into the egg collection belt area and the number and positions of eggs that do not fall into the egg collection belt area output by the behavior analysis model;

[0031] Among them, the behavior analysis model is built based on the target detection framework, and includes a feature extraction network, a feature aggregation network, a detection head and two segmentation heads. The behavior analysis model is used for:

[0032] Perform feature extraction and feature fusion on at least one consecutive video frame in the video data in sequence;

[0033] Based on the fused features, use the one detection head to detect and identify the chicken body, chicken head and eggs, obtain the position information of the chicken body, chicken head and eggs corresponding to each of the at least one video frame, and use the two segmentation heads to segment the feeding trough area and the egg collection belt area respectively to obtain the position information of the feeding trough area and the egg collection belt area;

[0034] Based on the position information of the chicken body, chicken head, eggs, feeding trough area and egg collection belt area corresponding to each of the at least one video frame, determine the position relationship between the chicken head and the feeding trough area, the position matching relationship between the chicken head and the chicken body, and the position relationship between the eggs and the egg collection belt area corresponding to each of the at least one video frame;

[0035] Based on the position relationship between the chicken head and the feeding trough area and the position matching relationship between the chicken head and the chicken body corresponding to each of the at least one video frame, determine the total feeding duration of laying hens in each cage position. Based on the position relationship between the eggs and the egg collection belt area corresponding to each of the at least one video frame, determine the number of eggs that fall into the egg collection belt area and the number and positions of eggs that do not fall into the egg collection belt area in each cage position.

[0036] The present invention also provides an electronic device, including a memory, a processor and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the automatic behavior monitoring method for caged laying hens as described in any one of the above.

[0037] The present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the automatic behavior monitoring method for caged laying hens as described in any one of the above.

[0038] The present invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the automatic behavior monitoring method for caged laying hens as described in any one of the above.

[0039] A method, device and electronic device for automatically monitoring the behavior of caged laying hens provided by the present invention input video data of the laying hen breeding scenario into a behavior analysis model, enabling the behavior analysis model to perform feature extraction and feature fusion on the video data, and then using a detection head and two segmentation heads to obtain the position information of the chicken body, chicken head, eggs, feeding trough area and egg collection belt area. Finally, based on the position information of the chicken body, chicken head, eggs, feeding trough area and egg collection belt area, the total feeding duration of the laying hens in each cage position, the number of eggs falling into the egg collection belt area in each cage position, the number and positions of eggs not falling into the egg collection belt area can be determined, which can improve the automation degree and monitoring accuracy of the behavior monitoring of caged laying hens. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0041] Figure 1 is one of the flow schematic diagrams of the method for automatically monitoring the behavior of caged laying hens provided by the present invention;

[0042] Figure 2 is another flow schematic diagram of the method for automatically monitoring the behavior of caged laying hens provided by the present invention;

[0043] Figure 3 is the overall framework schematic diagram of the behavior analysis model provided by the present invention;

[0044] Figure 4 is the schematic diagram of the result data provided by the present invention;

[0045] Figure 5 is the structural schematic diagram of the device for automatically monitoring the behavior of caged laying hens provided by the present invention;

[0046] Figure 6 is the structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] To make the objectives, technical solutions and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention in conjunction with the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments in the present invention belong to the scope of protection of the present invention.

[0048] First, the following content is introduced:

[0049] Laying hen farming is an industry of great significance to the national economy, and it is developing in the direction of large-scale, intelligent, and unmanned. In the related technologies, the main way to raise laying hens is cage farming. During the farming process, the intelligent perception of the behavior information of laying hens is of great significance for liberating the labor force, improving the automation level and efficiency of laying hen farming.

[0050] During the laying hen farming process, monitoring the health status of the raised chickens is an important link. Since chickens often show loss of appetite and other conditions before or in the early stage of illness, accurately detecting such situations can enable timely intervention and reduce losses. In addition, the egg-laying behavior is also an important indicator concerned in laying hen farming, and the change in egg production often indicates the possibility of disease occurrence. Therefore, monitoring the behavior of laying hens is an important task in the daily management of cage-raised laying hens.

[0051] In the related technologies of the poultry farming industry, manual observation and empirical judgment are often used to perceive the behavior information of chickens. However, with the continuous increase in the farming scale, the manual method is not only time-consuming and laborious, but also unable to ensure the integrity and timeliness of information perception, greatly affecting the efficiency of information perception. At the same time, since the labor force engaged in agriculture is rapidly decreasing, this also puts forward a more urgent demand for the poultry farming industry to transform from the traditional mode to a large-scale and intelligent production mode.

[0052] As an important branch of artificial intelligence, computer vision technology has developed rapidly in recent years and has been successfully applied in many industries, including security, retail, healthcare, agriculture, autonomous driving, etc., significantly improving the work efficiency, and its performance in many tasks has exceeded that of humans, greatly facilitating people's life and production. For example, in the field of autonomous driving, some scholars have proposed the panoramic driving perception model YOLOP, which can process road target detection, drivable area segmentation, and lane line detection tasks in the same framework, and can assist the vehicle to make reasonable decisions during driving.

[0053] Next, in combination with Figures 1 - 4 Describe the automatic behavior monitoring method for cage-raised laying hens provided by the present invention.

[0054] Figure 1 is one of the flow schematic diagrams of the automatic behavior monitoring method for cage-raised laying hens provided by the present invention. As Figure 1 shown, the method includes the following steps:

[0055] Step 100, obtain video data of the laying hen farming scenario, where the body of each laying hen in the laying hen farming scenario is located inside the cage;

[0056] Optionally, the laying hen breeding scenario can be a chicken coop, a chicken farm, or other laying hen breeding scenarios where chickens are raised in cages, and the present invention does not limit this.

[0057] Optionally, video data of the laying hen breeding scenario can be obtained through monitoring devices deployed in the laying hen breeding scenario, such as cameras or video collectors.

[0058] Optionally, after obtaining the video data of the laying hen breeding scenario, the video data can be saved in a memory or transmitted to a processing computer through a network for subsequent processing.

[0059] Optionally, after obtaining the video data of the laying hen breeding scenario, the video data can be identified and analyzed to achieve a panoramic perception of the laying hen breeding scenario, and then the daily activity information of the raised chickens can be monitored.

[0060] Step 110: Input at least one consecutive video frame image in the video data into a behavior analysis model, and obtain the total feeding duration of laying hens in each cage position, the number of eggs falling into the egg collection belt area in each cage position, the number and positions of eggs not falling into the egg collection belt area output by the behavior analysis model;

[0061] Among them, the behavior analysis model is built based on an object detection framework, including a feature extraction network, a feature aggregation network, a detection head, and two segmentation heads. The behavior analysis model is used for:

[0062] Respectively and sequentially perform feature extraction and feature fusion on at least one consecutive video frame image in the video data;

[0063] Based on the fused features, use the one detection head to detect and identify the chicken body, chicken head, and eggs, obtain the position information of the chicken body, chicken head, and eggs corresponding to each of the at least one video frame image, and use the two segmentation heads to respectively segment the feeding trough area and the egg collection belt area to obtain the position information of the feeding trough area and the egg collection belt area;

[0064] Based on the position information of the chicken body, chicken head, eggs, feeding trough area, and egg collection belt area corresponding to each of the at least one video frame image, determine the position relationship between the chicken head and the feeding trough area, the position matching relationship between the chicken head and the chicken body, and the position relationship between the eggs and the egg collection belt area corresponding to each of the at least one video frame image;

[0065] Based on the positional relationship between the chicken head and the feeder area corresponding to each of the at least one video frame and the positional matching relationship between the chicken head and the chicken body, determine the total feeding duration of the laying hens in each cage position. Based on the positional relationship between the eggs and the egg collecting belt area corresponding to each of the at least one video frame, determine the number of eggs that have fallen into the egg collecting belt area and the number and positions of the eggs that have not fallen into the egg collecting belt area for each cage position.

[0066] Figure 2 It is the second flow schematic diagram of the automatic monitoring method for the behavior of caged laying hens provided by the present invention. As Figure 2 shown, in an embodiment of the present invention, the method steps include feature extraction, feature fusion, target detection, region segmentation, etc., and processing according to the detection and segmentation results to obtain the feeding and egg laying information of the laying hens in each cage position.

[0067] Optionally, the video data of the laying hen breeding scenario obtained contains multiple video frames. Some consecutive video frame images can be extracted therefrom, or the video frame images included in the entire video data can be input into the behavior analysis model to obtain the total feeding duration of the laying hens in each cage position, the number of eggs that have fallen into the egg collecting belt area and the number and positions of the eggs that have not fallen into the egg collecting belt area for each cage position output by the behavior analysis model.

[0068] Optionally, the video data of the laying hen breeding scenario can be frame-decoded sequentially to obtain at least one consecutive video frame image in the video data.

[0069] Optionally, the present invention uses a behavior analysis model to identify and analyze at least one consecutive video frame image in the video data, obtain the feeding and egg laying related information of the chickens in each cage position in at least one video frame, and further statistically obtain the daily activity information of the chickens in each cage position within a certain period.

[0070] Optionally, the behavior analysis model is built with a target detection framework. The target detection framework can be Yolov5, or Yolov3, or Yolox, or other target detection frameworks, and the present invention does not limit this.

[0071] In an embodiment of the present invention, a modified Yolov5 is used as the target detection framework of the behavior analysis model. Specifically, some components in Yolov5 are replaced, and the basic architecture is retained so that it can complete the tasks of detection and segmentation simultaneously.

[0072] Optionally, the behavior analysis model can use the same feature extraction network and multi-scale feature fusion network to perform feature extraction and feature fusion on each input video frame image, and obtain features of different scales with stronger semantic information to adapt to targets of different sizes.

[0073] In one embodiment of the present invention, the feature extraction network and the multi-scale feature fusion network used in the behavior analysis model are the same as those in Yolov5. The CSPDarknet53 with a Focus structure is used as the backbone network for feature extraction. In the feature fusion part, Spatial Pyramid Pooling (SPP) is used to convert feature maps of any size into fixed-size feature vectors to adapt to pictures of different sizes. Then, the Feature Pyramid Networks (FPN) introducing the CSP2 structure is used to fuse features of different scales.

[0074] Optionally, the behavior analysis model can introduce a detection head and two segmentation heads in the output part to share the same feature extraction network and multi-scale feature fusion network in a multi-task learning manner, which are respectively used to achieve object detection and region segmentation.

[0075] Figure 3 is a schematic diagram of the overall framework of the behavior analysis model provided by the present invention. As Figure 3 shown, in one embodiment of the present invention, the behavior analysis model mainly includes four main parts: a feature extraction network, a multi-scale feature fusion module, a detection head, and a segmentation head. The detection head uses feature maps of different scales for detection.

[0076] Optionally, the task of detecting and identifying chicken heads, chicken bodies, and eggs is implemented by a multi-class detection head based on the fused feature maps. A detection head without anchor boxes similar to that in Yolox can be used to obtain higher accuracy and convergence speed. At each feature point, the target category and the position of the target box are predicted respectively. For the target category prediction, the Sigmoid activation function can be used, and the regression amount of the target box position is the coordinate offset of the target box relative to the upper left corner of the grid and the height and width of the box, so as to obtain the position information of the chicken body, chicken head, and eggs.

[0077] Specifically, the classification branch can use three Sigmoid activation functions to predict the probabilities that a certain candidate box belongs to a chicken body, a chicken head, and an egg respectively. The regression branch uses a Sigmoid to predict the objectness score, which is used to evaluate the probability that the candidate box belongs to a foreground object. The four bounding box regression branches predict the coordinate offset of the target box relative to the upper left corner of the grid and the height and width of the box.

[0078] Optionally, the task of segmenting the feeding trough and egg collecting belt regions is implemented by two segmentation heads based on the fused highest-resolution feature maps. Each segmentation head restores the output feature map to the original image size through an upsampling layer, and then predicts the foreground regions of the feeding trough and egg collecting belt regions respectively to obtain the position information of the feeding trough region and the egg collecting belt region.

[0079] Optionally, the feeding trough area is used to load the feed for the laying hens, and the egg collecting belt area is used to load the eggs produced by the laying hens.

[0080] Optionally, after obtaining the position information of the chicken body, chicken head, eggs, feeding trough area, and egg collecting belt area corresponding to each video frame, the behavior analysis model can determine the positional relationship between the chicken head and the feeding trough area. Specifically, it can match the chicken head with the feeding trough area to determine the chicken head that is eating.

[0081] Optionally, since there are many cases where chickens stretch their necks to the side when eating, the position of the chicken head can only be used to judge whether a chicken is eating and cannot represent the actual position of the eating chicken. To obtain the position of the eating chicken in the cage, the position of its chicken body should be used to represent it. Therefore, the positional matching relationship between the chicken head and the chicken body can be determined to determine which cage position the eating chicken head specifically belongs to.

[0082] Optionally, based on the eating chicken heads in each cage position in multiple video frames, the total eating duration of the laying hens in each cage position can be determined.

[0083] Optionally, after obtaining the position information of the chicken body, chicken head, eggs, feeding trough area, and egg collecting belt area corresponding to each video frame, the behavior analysis model can determine the positional relationship between the eggs and the egg collecting belt area, and then based on the positional relationship between the eggs and the egg collecting belt area, determine the number of eggs that fall into the egg collecting belt area and the number and positions of the eggs that do not fall into the egg collecting belt area for each cage position.

[0084] Optionally, the positional relationship between the eggs and the egg collecting belt area can be that the eggs are located within the egg collecting belt area or the eggs are located outside the egg collecting belt area.

[0085] Optionally, after obtaining the total eating duration of the laying hens in each cage position, the number of eggs that fall into the egg collecting belt area and the number and positions of the eggs that do not fall into the egg collecting belt area for each cage position, the statistics and analysis of the eating and egg-laying results for each cage position can be performed.

[0086] Figure 4 is a schematic diagram of the result data provided by the present invention. As Figure 4 shown, in an embodiment of the present invention, the abscissa is set along the horizontal direction of the monitored chicken cage area, and the chicken cage area is divided into a certain number of parts according to the cage partition positions on the abscissa, and a bar chart of the eating time is drawn. The height of each bar indicates the total number of frames of the chickens eating in this horizontal area. Since the frame rate is stable, the total eating time of all chickens in this area can be directly calculated.

[0087] The automatic behavior monitoring method for caged laying hens provided by the present invention inputs the video data of the laying hen breeding scenario into a behavior analysis model, enabling the behavior analysis model to perform feature extraction and feature fusion on the video data, and then uses one detection head and two segmentation heads to obtain the position information of the chicken body, chicken head, eggs, feeding trough area, and egg collection belt area. Finally, based on the position information of the chicken body, chicken head, eggs, feeding trough area, and egg collection belt area, the total feeding duration of the laying hens in each cage position, the number of eggs falling into the egg collection belt area in each cage position, and the number and positions of the eggs not falling into the egg collection belt area can be determined, which can improve the automation degree and monitoring accuracy of the behavior monitoring of caged laying hens.

[0088] Optionally, after performing feature extraction and feature fusion on at least one consecutive video frame image in the video data in sequence, three different scales of fused features are obtained;

[0089] Based on the fused features, using the one detection head to detect and identify the chicken body, chicken head, and eggs, and obtaining the position information of the chicken body, chicken head, and eggs corresponding to each of the at least one video frame image, including:

[0090] Based on the three different scales of fused features, using the one detection head to detect and identify the chicken body, chicken head, and eggs, and obtaining the position information of the chicken body, chicken head, and eggs corresponding to each of the at least one video frame image.

[0091] Optionally, the behavior analysis model can perform feature extraction and feature fusion on at least one consecutive video frame image in the video data in sequence, and obtain three different scales of fused features with stronger semantic information to adapt to targets of different sizes.

[0092] Optionally, the behavior analysis model can use one detection head to detect and identify the chicken body, chicken head, and eggs based on three different scales of fused features, and obtain the position information of the chicken body, chicken head, and eggs corresponding to at least one video frame image.

[0093] The automatic behavior monitoring method for caged laying hens provided by the present invention performs feature extraction and feature fusion on the video frame images of the laying hen breeding scenario, obtains three different scales of fused features with stronger semantic information to adapt to targets of different sizes, and then based on the three different scales of fused features, uses one detection head to obtain the position information of the chicken body, chicken head, and eggs corresponding to at least one video frame image.

[0094] Optionally, the using the two segmentation heads to respectively segment the feeding trough area and the egg collection belt area, and obtaining the position information of the feeding trough area and the egg collection belt area, includes:

[0095] Based on the fusion feature with the highest resolution among the three different-scale fusion features, use the two segmentation heads to segment the feeding trough area and the egg collection belt area respectively, and obtain the position information of the feeding trough area and the egg collection belt area.

[0096] Optionally, both segmentation heads of the behavior analysis model take the output feature of the layer with the highest resolution in the feature pyramid as input, restore the output feature map to the original image size through three upsampling layers, and predict the foreground areas of the feeding trough and the egg collection belt areas respectively to obtain the position information of the feeding trough area and the egg collection belt area.

[0097] Optionally, the size of the feature map with the highest resolution is 1 / 8 of the original image. After being upsampled through three 3×3 convolutions and the nearest neighbor interpolation method, it is restored to the same size as the original image, so as to obtain the segmentation result of the specified area.

[0098] The automatic behavior monitoring method for caged laying hens provided by the present invention obtains three different-scale fusion features with stronger semantic information through feature extraction and feature fusion of the video frame images of the laying hen breeding scene to adapt to targets of different sizes, and then based on the fusion feature with the highest resolution among the three different-scale fusion features, obtains the position information of the feeding trough area and the egg collection belt area through two segmentation heads.

[0099] Optionally, determining the position matching relationship between the chicken head and the chicken body based on the position information of the chicken body, chicken head, eggs, feeding trough area, and egg collection belt area respectively corresponding to at least one video frame image includes:

[0100] Calculate the intersection over union (IoU) between the chicken body and the chicken head respectively corresponding to at least one video frame image;

[0101] Based on the IoU, use the Hungarian algorithm to match the chicken head with the chicken body to determine the position matching relationship between the chicken head and the chicken body.

[0102] Optionally, in order to obtain the position matching relationship between the chicken head and the chicken body in the video frame image, the IoU between the detected chicken head and the detected chicken body can be calculated first.

[0103] Optionally, the IoU is the most commonly used evaluation metric in tasks such as object detection, semantic segmentation, and tracking. It is the ratio of the intersection to the union of two regions, and the IoU is the largest when the two regions completely overlap.

[0104] Optionally, after obtaining the IoU of the chicken head and the chicken body, based on the IoU, the chicken body and the chicken head can be matched through the Hungarian algorithm to determine the position of the chicken body of the feeding chicken, and record the center point coordinates of its detection box.

[0105] The automatic behavior monitoring method for caged laying hens provided by the present invention calculates the intersection over union (IoU) of the detected chicken heads and the detected chicken bodies respectively, and based on the IoU, matches the chicken bodies and chicken heads through the Hungarian algorithm to determine the position of the chicken body of the feeding chicken and the positional relationship between the chicken body and the chicken head, which is convenient for subsequent determination of the total feeding duration of the laying hens in each cage position.

[0106] Optionally, determining the total feeding duration of the laying hens in each cage position based on the positional relationship between the chicken head and the feeding trough area and the positional matching relationship between the chicken head and the chicken body corresponding to each of the at least one video frame includes:

[0107] Regarding the chicken heads with their positions within the feeding trough area in the at least one video frame as feeding chicken heads;

[0108] Based on the positional matching relationship between the feeding chicken heads and the chicken bodies, determining the position of the chicken body corresponding to the feeding chicken head and the cage position to which it belongs;

[0109] Based on the duration of the at least one video frame, the feeding chicken heads in the at least one video frame, the position of the chicken body corresponding to the feeding chicken head, and the cage position to which it belongs, determining the total feeding duration of the laying hens in each cage position.

[0110] Optionally, the chicken head and the feeding trough area can be matched. If a chicken head detected within the feeding trough area is detected, it can be recognized as a feeding chicken head, that is, the chicken is eating.

[0111] Optionally, the detected chicken head position can be compared with the feeding trough area. If the center point of a certain chicken head detection frame is within the upper and lower boundaries of the feeding area of the feeding trough, that chicken head is recognized as a feeding chicken head.

[0112] Optionally, in order to more accurately determine the feeding state, an effective feeding area can be further specified within the feeding trough. This area has a certain distance from the upper and lower boundaries of the feeding trough, and the appropriate boundary distance can be determined by manually observing the actual feeding video.

[0113] Optionally, since there are many situations where chickens stretch their necks to the side when feeding, the position of the chicken head can only be used to judge whether a chicken is feeding and cannot represent the actual position of the feeding chicken. In order to obtain the position of the feeding chicken in the cage, the position of its chicken body should be used to represent it. Therefore, the positional matching relationship between the chicken head and the chicken body can be used to determine which cage position the feeding chicken head specifically belongs to.

[0114] Optionally, the center coordinates of the chicken body detection frame can be recorded as the position of the feeding chicken to determine which cage position it belongs to.

[0115] Optionally, the total feeding duration of laying hens in each cage can be determined based on the feeding chicken heads in each cage position among multiple video frame images.

[0116] For example, if each video frame is 1 / 12 second, and in at least one video frame, among 120 video frame images, the chicken heads in cage A are located within the feeding trough area, then the total feeding duration of the laying hens in cage A can be determined to be 10 seconds.

[0117] The automatic behavior monitoring method for caged laying hens provided by the present invention takes the chicken heads with their positions within the feeding trough area as feeding chicken heads, determines the body positions and the corresponding cage positions of the feeding chicken heads based on the position matching relationship between the feeding chicken heads and the chicken bodies, and finally determines the total feeding duration of the laying hens in each cage based on the duration of the video frame images.

[0118] Optionally, based on the position relationship between the eggs and the egg collection belt area corresponding to each of the at least one video frame image, determining the number of eggs that have fallen into the egg collection belt area and the number and positions of the eggs that have not fallen into the egg collection belt area in each cage includes:

[0119] Dividing the egg collection belt area according to the demarcation between cages;

[0120] Based on the number of eggs located in the egg collection belt areas corresponding to each cage in the one or more video frame images with the latest time among the at least one video frame image, determining the number of eggs that have fallen into the egg collection belt area in each cage, and based on the number and positions of the eggs located outside the egg collection belt area in the one or more video frame images with the latest time among the at least one video frame image, determining the number and positions of the eggs that have not fallen into the egg collection belt area.

[0121] Optionally, after obtaining the position information of the chicken bodies, chicken heads, eggs, feeding trough areas, and egg collection belt areas corresponding to each video frame image, the behavior analysis model can determine the position relationship between the eggs and the egg collection belt area.

[0122] Specifically, the egg collection belt area can be divided according to the demarcation between cages. For the eggs located within the egg collection belt, the egg production volume of each cage is counted according to their positions within the egg collection belt, and at the same time, the eggs located outside the egg collection belt are identified as the eggs that have not fallen into the egg collection belt.

[0123] Optionally, the behavior analysis model can compare the egg detection frame with the boundary of the egg collection belt area to determine whether the eggs are in a normal area. For the eggs in the normal area, record the coordinates of their corresponding cages, and for the eggs with abnormal positions, also record their positions.

[0124] Optionally, since the number of eggs in the last frame or the last few frames of consecutive video frames can represent the total number of eggs finally, the egg production can be calculated according to the egg detection results of the last frame or several frames, and at the same time, the positions of the eggs that do not fall into the egg collection belt are output.

[0125] In an embodiment of the present invention, the egg production situation is counted for several frames before collecting eggs using the conveyor belt in the egg collection belt, and the mode of the number of eggs detected in the egg collection belt in these frames is used as the egg production corresponding to each cage position. At the same time, the positions of the eggs outside the egg collection belt are output as abnormal information.

[0126] The automatic behavior monitoring method for caged laying hens provided by the present invention divides the egg collection belt area according to the boundaries between cage positions, and determines the number of eggs falling into the egg collection belt area of each cage position based on the number of eggs in the egg collection belt areas corresponding to each cage position in the latest one or more video frame images among at least one video frame image. Based on the number and positions of the eggs outside the egg collection belt area in the latest one or more video frame images among at least one video frame image, the number and positions of the eggs that do not fall into the egg collection belt area are determined.

[0127] Next, the automatic behavior monitoring device for caged laying hens provided by the present invention is described. The automatic behavior monitoring device for caged laying hens described below can be correspondingly referred to the automatic behavior monitoring method for caged laying hens described above.

[0128] Figure 5 is a schematic structural diagram of the automatic behavior monitoring device for caged laying hens provided by the present invention, as Figure 5 shown, the device includes an acquisition module 510 and an input module 520, wherein:

[0129] The acquisition module 510 is used to acquire video data of the laying hen breeding scenario, and the body of each laying hen in the laying hen breeding scenario is located in the cage;

[0130] The input module 520 is used to input at least one consecutive video frame image in the video data into the behavior analysis model, and obtain the total feeding duration of laying hens in each cage position, the number of eggs falling into the egg collection belt area of each cage position, and the number and positions of the eggs that do not fall into the egg collection belt area output by the behavior analysis model;

[0131] Among them, the behavior analysis model is built based on the object detection framework and includes a feature extraction network, a feature aggregation network, a detection head and two segmentation heads. The behavior analysis model is used for:

[0132] Perform feature extraction and feature fusion on at least one consecutive video frame image in the video data in sequence;

[0133] Based on the fused features, use the one detection head to detect and identify the chicken body, chicken head and eggs, obtain the position information of the chicken body, chicken head and eggs corresponding to each of the at least one video frame, and use the two segmentation heads to segment the feeding trough area and the egg collecting belt area respectively, to obtain the position information of the feeding trough area and the egg collecting belt area;

[0134] Based on the position information of the chicken body, chicken head, eggs, feeding trough area and egg collecting belt area corresponding to each of the at least one video frame, determine the position relationship between the chicken head and the feeding trough area, the position matching relationship between the chicken head and the chicken body, and the position relationship between the eggs and the egg collecting belt area corresponding to each of the at least one video frame;

[0135] Based on the position relationship between the chicken head and the feeding trough area and the position matching relationship between the chicken head and the chicken body corresponding to each of the at least one video frame, determine the total feeding duration of the laying hens in each cage position. Based on the position relationship between the eggs and the egg collecting belt area corresponding to each of the at least one video frame, determine the number of eggs that fall into the egg collecting belt area and the number and positions of the eggs that do not fall into the egg collecting belt area in each cage position.

[0136] An automatic behavior monitoring device for caged laying hens provided by the present invention inputs video data of the laying hen breeding scenario into a behavior analysis model, enables the behavior analysis model to perform feature extraction and feature fusion on the video data, then uses one detection head and two segmentation heads to obtain the position information of the chicken body, chicken head, eggs, feeding trough area and egg collecting belt area, and finally determines the total feeding duration of the laying hens in each cage position, the number of eggs that fall into the egg collecting belt area in each cage position, and the number and positions of the eggs that do not fall into the egg collecting belt area based on the position information of the chicken body, chicken head, eggs, feeding trough area and egg collecting belt area, which can improve the automation degree and monitoring accuracy of the behavior monitoring of caged laying hens.

[0137] It can be understood that the automatic behavior monitoring device for caged laying hens provided by the present invention corresponds to the automatic behavior monitoring method for caged laying hens provided in the above embodiments. The relevant technical features of the automatic behavior monitoring device for caged laying hens provided by the present invention can refer to the relevant technical features of the automatic behavior monitoring method for caged laying hens provided in the above embodiments, and will not be elaborated here.

[0138] Figure 6 Illustrates a schematic physical structure diagram of an electronic device, such as Figure 6As shown in the figure, the electronic device may include: a processor 610, a communications interface 620, a memory 630, and a communication bus 640. Among them, the processor 610, the communications interface 620, and the memory 630 communicate with each other through the communication bus 640. The processor 610 can call the logical instructions in the memory 630 to execute the automatic monitoring method for the behaviors of caged laying hens. The method includes: obtaining video data of the laying hen breeding scenario, where the body of each laying hen in the laying hen breeding scenario is located inside the cage; inputting at least one consecutive video frame image in the video data into a behavior analysis model to obtain the total feeding duration of the laying hens in each cage position, the number of eggs that fall into the egg collecting belt area in each cage position, and the number and positions of the eggs that do not fall into the egg collecting belt area output by the behavior analysis model; where the behavior analysis model is built based on an object detection framework and includes a feature extraction network, a feature aggregation network, a detection head, and two segmentation heads. The behavior analysis model is used to: respectively and sequentially perform feature extraction and feature fusion on at least one consecutive video frame image in the video data; based on the fused features, use the detection head to detect and identify the chicken body, chicken head, and eggs, obtain the position information of the chicken body, chicken head, and eggs corresponding to each of the at least one video frame image, and use the two segmentation heads to respectively segment the feeding trough area and the egg collecting belt area to obtain the position information of the feeding trough area and the egg collecting belt area; based on the position information of the chicken body, chicken head, eggs, feeding trough area, and egg collecting belt area corresponding to each of the at least one video frame image, determine the position relationship between the chicken head and the feeding trough area, the position matching relationship between the chicken head and the chicken body, and the position relationship between the eggs and the egg collecting belt area corresponding to each of the at least one video frame image; based on the position relationship between the chicken head and the feeding trough area and the position matching relationship between the chicken head and the chicken body corresponding to each of the at least one video frame image, determine the total feeding duration of the laying hens in each cage position, and based on the position relationship between the eggs and the egg collecting belt area corresponding to each of the at least one video frame image, determine the number of eggs that fall into the egg collecting belt area in each cage position and the number and positions of the eggs that do not fall into the egg collecting belt area.

[0139] In addition, when the logical instructions in the aforementioned memory 630 are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

[0140] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the automatic behavior monitoring method for caged laying hens provided by the above-mentioned various methods. The method includes: obtaining video data of the laying hen breeding scenario, where the body of each laying hen in the laying hen breeding scenario is located inside the cage; inputting at least one consecutive video frame image in the video data into a behavior analysis model to obtain the total feeding duration of the laying hens in each cage position, the number of eggs that fall into the egg collection belt area and the number and positions of the eggs that do not fall into the egg collection belt area output by the behavior analysis model; wherein, the behavior analysis model is built based on a target detection framework and includes a feature extraction network, a feature aggregation network, a detection head and two segmentation heads. The behavior analysis model is used for: respectively and sequentially performing feature extraction and feature fusion on at least one consecutive video frame image in the video data; based on the fused features, using the one detection head to detect and identify the chicken body, chicken head and eggs, obtaining the position information of the chicken body, chicken head and eggs corresponding to the at least one video frame image respectively, and using the two segmentation heads to respectively segment the feeding trough area and the egg collection belt area to obtain the position information of the feeding trough area and the egg collection belt area; based on the position information of the chicken body, chicken head, eggs, feeding trough area and egg collection belt area corresponding to the at least one video frame image respectively, determining the position relationship between the chicken head and the feeding trough area, the position matching relationship between the chicken head and the chicken body, and the position relationship between the eggs and the egg collection belt area corresponding to the at least one video frame image respectively; based on the position relationship between the chicken head and the feeding trough area and the position matching relationship between the chicken head and the chicken body corresponding to the at least one video frame image respectively, determining the total feeding duration of the laying hens in each cage position, and based on the position relationship between the eggs and the egg collection belt area corresponding to the at least one video frame image respectively, determining the number of eggs that fall into the egg collection belt area and the number and positions of the eggs that do not fall into the egg collection belt area in each cage position.

[0141] In another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements an automatic behavior monitoring method for caged laying hens provided by the above-mentioned various methods. The method includes: obtaining video data of the laying hen breeding scenario, where the body of each laying hen in the laying hen breeding scenario is located inside the cage; inputting at least one consecutive video frame image in the video data into a behavior analysis model, and obtaining the total feeding duration of the laying hens in each cage position, the number of eggs that fall into the egg collecting belt area and the number and positions of the eggs that do not fall into the egg collecting belt area output by the behavior analysis model; wherein, the behavior analysis model is built based on a target detection framework, and includes a feature extraction network, a feature aggregation network, a detection head and two segmentation heads. The behavior analysis model is used for: respectively and sequentially performing feature extraction and feature fusion on at least one consecutive video frame image in the video data; based on the fused features, using the one detection head to detect and identify the chicken body, chicken head and eggs, and obtaining the position information of the chicken body, chicken head and eggs corresponding to the at least one video frame image respectively, and using the two segmentation heads to respectively segment the feeding trough area and the egg collecting belt area to obtain the position information of the feeding trough area and the egg collecting belt area; based on the position information of the chicken body, chicken head, eggs, feeding trough area and egg collecting belt area corresponding to the at least one video frame image respectively, determining the position relationship between the chicken head and the feeding trough area, the position matching relationship between the chicken head and the chicken body, and the position relationship between the eggs and the egg collecting belt area corresponding to the at least one video frame image respectively; based on the position relationship between the chicken head and the feeding trough area and the position matching relationship between the chicken head and the chicken body corresponding to the at least one video frame image respectively, determining the total feeding duration of the laying hens in each cage position, and based on the position relationship between the eggs and the egg collecting belt area corresponding to the at least one video frame image respectively, determining the number of eggs that fall into the egg collecting belt area and the number and positions of the eggs that do not fall into the egg collecting belt area in each cage position.

[0142] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0143] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for automatically monitoring the behavior of caged laying hens, characterized in that: include: Acquire video data of a laying hen breeding scene, where the body of each laying hen in the laying hen breeding scene is located in a cage; Inputting at least one continuous video frame in the video data into a behavior analysis model, and obtaining the total feeding time of laying hens in each cage, the number of eggs falling into the egg collecting belt area of ​​each cage, and the number and position of eggs not falling into the egg collecting belt area, which are output by the behavior analysis model; The behavior analysis model is built based on the target detection framework, including a feature extraction network, a feature aggregation network, a detection head and two segmentation heads. The behavior analysis model is used to: Performing feature extraction and feature fusion on at least one continuous video frame in the video data in sequence; Based on the fused features, the one detection head is used to detect and identify the chicken body, chicken head and eggs, and the position information of the chicken body, chicken head and eggs corresponding to the at least one video frame is obtained, and the two segmentation heads are used to segment the feeding trough area and the egg collecting belt area respectively, and the position information of the feeding trough area and the egg collecting belt area is obtained; Based on the position information of the chicken body, chicken head, eggs, feeding trough area and egg collecting belt area respectively corresponding to the at least one video frame, determine the position relationship between the chicken head and the feeding trough area, the position matching relationship between the chicken head and the chicken body, and the position relationship between the eggs and the egg collecting belt area respectively corresponding to the at least one video frame; Based on the positional relationship between the chicken head and the feeding trough area corresponding to the at least one video frame and the position matching relationship between the chicken head and the chicken body, the total feeding time of the laying hen in each cage is determined; based on the positional relationship between the eggs and the egg collecting belt area corresponding to the at least one video frame, the number of eggs that fall into the egg collecting belt area and the number and positions of eggs that do not fall into the egg collecting belt area in each cage are determined.

2. The method for automatically monitoring the behavior of caged laying hens according to claim 1, characterized in that: After extracting and fusing features of at least one continuous video frame in the video data in sequence, three fusion features of different scales are obtained; The method of detecting and identifying the chicken body, the chicken head and the egg using the one detection head based on the fused features, and obtaining the position information of the chicken body, the chicken head and the egg respectively corresponding to the at least one video frame, includes: Based on the fusion features of the three different scales, the chicken body, chicken head and eggs are detected and identified using the one detection head to obtain position information of the chicken body, chicken head and eggs corresponding to the at least one video frame.

3. The method for automatically monitoring the behavior of caged laying hens according to claim 2, characterized in that: The method of using the two segmentation heads to segment the feeding trough area and the egg collecting belt area respectively to obtain the position information of the feeding trough area and the egg collecting belt area includes: Based on the fusion feature with the highest resolution among the fusion features of the three different scales, the two segmentation heads are used to segment the feeding trough area and the egg collecting belt area respectively to obtain the position information of the feeding trough area and the egg collecting belt area.

4. The method for automatically monitoring the behavior of caged laying hens according to claim 1, characterized in that: The determining of the position matching relationship between the chicken head and the chicken body based on the position information of the chicken body, the chicken head, the eggs, the feeding trough area and the egg collecting belt area respectively corresponding to the at least one video frame includes: Calculate the intersection-over-union ratio between the chicken body and the chicken head corresponding to the at least one video frame; Based on the intersection-over-union ratio, the chicken head and the chicken body are matched using the Hungarian algorithm to determine the position matching relationship between the chicken head and the chicken body.

5. The method for automatically monitoring the behavior of caged laying hens according to claim 1, characterized in that: The determining of the total feeding time of the laying hens in each cage based on the positional relationship between the chicken head and the feeding trough area and the positional matching relationship between the chicken head and the chicken body respectively corresponding to the at least one video frame includes: The chicken head whose chicken head is located in the feeding trough area in the at least one video frame is used as the feeding chicken head; Based on the position matching relationship between the feeding chicken head and the chicken body, determine the chicken body position corresponding to the feeding chicken head and the cage position to which it belongs; Based on the duration of the at least one video frame, the feeding chicken head in the at least one video frame, the chicken body position corresponding to the feeding chicken head, and the cage to which it belongs, the total feeding time of the laying hen in each cage is determined.

6. The method for automatically monitoring the behavior of caged laying hens according to claim 1, characterized in that: The method of determining the number of eggs in each cage that fall into the egg collecting belt area and the number and position of eggs that do not fall into the egg collecting belt area based on the positional relationship between the eggs and the egg collecting belt area respectively corresponding to the at least one video frame includes: Dividing the egg collecting belt area according to the boundaries between cage positions; Based on the number of eggs located in the egg collecting belt area corresponding to each cage position in one or more video frames with the latest time in the at least one video frame, the number of eggs falling into the egg collecting belt area in each cage position is determined; based on the number of eggs located outside the egg collecting belt area in one or more video frames with the latest time in the at least one video frame, the number and positions of eggs that have not fallen into the egg collecting belt area are determined.

7. An automatic behavior monitoring device for caged laying hens, characterized in that: include: An acquisition module is used to acquire video data of a laying hen breeding scene, in which the body of each laying hen is located in a cage; An input module is used to input at least one continuous video frame in the video data into a behavior analysis model to obtain the total feeding time of laying hens in each cage, the number of eggs in each cage that fall into the egg collecting belt area, and the number and position of eggs that do not fall into the egg collecting belt area, which are output by the behavior analysis model; The behavior analysis model is built based on the target detection framework, including a feature extraction network, a feature aggregation network, a detection head and two segmentation heads. The behavior analysis model is used to: Performing feature extraction and feature fusion on at least one continuous video frame in the video data in sequence; Based on the fused features, the one detection head is used to detect and identify the chicken body, chicken head and eggs, and the position information of the chicken body, chicken head and eggs corresponding to the at least one video frame is obtained, and the two segmentation heads are used to segment the feeding trough area and the egg collecting belt area respectively, and the position information of the feeding trough area and the egg collecting belt area is obtained; Based on the position information of the chicken body, chicken head, eggs, feeding trough area and egg collecting belt area respectively corresponding to the at least one video frame, determine the position relationship between the chicken head and the feeding trough area, the position matching relationship between the chicken head and the chicken body, and the position relationship between the eggs and the egg collecting belt area respectively corresponding to the at least one video frame; Based on the positional relationship between the chicken head and the feeding trough area corresponding to the at least one video frame and the position matching relationship between the chicken head and the chicken body, the total feeding time of the laying hen in each cage is determined; based on the positional relationship between the eggs and the egg collecting belt area corresponding to the at least one video frame, the number of eggs that fall into the egg collecting belt area and the number and positions of eggs that do not fall into the egg collecting belt area in each cage are determined.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the automatic behavior monitoring method for caged laying hens as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for automatically monitoring the behavior of caged laying hens as described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for automatically monitoring the behavior of caged laying hens as described in any one of claims 1 to 6 is implemented.

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