105 results about "Animal identification" patented technology

The invention discloses a wildlife detection method fusing an unmanned aerial vehicle thermal infrared image and a visible light image, and belongs to the field of small target wildlife identification, and the method comprises the following steps: S1, obtaining a preprocessed TIR-RGB image pair set; s2, an FDM-YOLO double-source target detection model improved based on YOLOv81 is constructed, and the improved FDM-YOLO double-source target detection model is trained based on the preprocessed TIR-RGB image pair set obtained in the step S1; and S3, inputting an image acquired in real time into the improved FDM-YOLO double-source target detection model trained in the step S2, and outputting a wild animal detection result. By adopting the wild animal detection method fusing the thermal infrared image and the visible light image of the unmanned aerial vehicle, high-precision, real-time and robust detection of a small target of a wild animal in a complex field environment is realized by improving the FDM-YOLO model.

The invention provides an endangered animal monitoring method based on a multi-mode floating gate type photoelectric synapse transistor, realizes multi-mode fusion of images (optical signals) and sounds (electric signals) by utilizing the light absorption capability of quantum dots and the high carrier mobility of a two-dimensional channel material, and belongs to the technical field of sensing, storage and calculation integration and endangered animal identification and monitoring. According to the method, a photoelectric synapse device is used as a reservoir, and input time sequence signals are respectively applied to a multi-mode fused photoelectric synapse transistor in the form of light and electric pulse. And along with the co-stimulation of light and electric pulse, the change response of source-drain current is generated, and the fusion processing of the multi-mode signal is realized. By adopting the method, a human multi-sensory fusion signal processing mode is successfully simulated, and accurate recognition under complex and changeable conditions can be realized by utilizing a mode of complementation of visual signals and auditory signals. Under an extreme environment or a high noise condition, edge processing and identification can be carried out on pictures and sounds of endangered animals, and a high-accuracy monitoring result can be obtained without a large amount of data return, so that the energy consumption is reduced and the monitoring efficiency is enhanced.

A marine zooplankter identification method based on in-situ images and deep learning is used for processing a multi-stage series deep learning architecture constructed for all in-situ images, and comprises the following steps: quickly positioning and framing zooplankter individuals under a complex background based on a YOLOv8 skeleton construction model; performing pixel-level fine segmentation on individuals in the in-situ image by using a U-Net model, and extracting an accurate contour to obtain a high-quality individual image; and finally, inputting the image into a PlanktonNet network innovatively designed based on a ViT architecture, and through adaptive small-size slice embedding and introduction of a convolution word embedding layer, improving small-scale target feature extraction capability, and realizing high-precision and fine-grained classification of genera and species. The method has the advantages that target detection, semantic segmentation and recognition tasks are organically fused, the problems of low recognition efficiency and low automation degree in the prior art are effectively solved, and marine zooplankton can be quickly and accurately recognized from in-situ images on a large scale.

The invention relates to the technical field of vehicle intelligent control, and provides an animal repelling method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining multi-modal data on a vehicle driving path; based on the multi-modal data, identifying whether a target animal hindering passage exists on the vehicle driving path, and if yes, determining attribute information and scene parameters of the target animal according to the multi-modal data; based on the attribute information and the scene parameters, generating a repelling signal for the target animal; and executing corresponding expelling operation based on the expelling signal. According to the method, the accuracy and the robustness of animal identification are improved by comprehensively utilizing the multi-modal data on the vehicle driving path, and a more targeted and more intelligent self-adaptive expelling signal is generated based on deep understanding of animal attributes and scene parameters, so that the success rate and the effectiveness of expelling can be greatly improved.

The invention relates to the field of backfat thickness detection, in particular to a backfat thickness detection system and method. The method comprises the following steps: setting a channel detection region, collecting animal image data and animal identity information through the channel detection region, analyzing the animal image data and the animal identity information, and constructing a three-dimensional target model; extracting the three-dimensional target model to obtain a three-dimensional target animal area, and identifying the three-dimensional target animal area to obtain a backfat detection area; processing the backfat detection area to obtain a backfat perspective model and a back section point set; in combination with a backfat perspective model, analyzing the back section point set to obtain a back boundary point set; and obtaining a detection thickness value according to the back boundary point set. According to the invention, the backfat thickness detection efficiency can be improved.

A repellence system and method for repelling animals includes an imaging device arranged to generate image data from a railway track, one or more deterrence devices arranged to carry out deterrence actions for repelling animals and a repellence sub-system having one or more processors and memory storing instructions for execution by the one or more processors. The repellence sub-system being configured to receive image data of the railway track from the imaging device, detect an animal in the image data, identify animal species of the detected animal in the image data, provide species specific deterrence instructions to the one or more deterrence devices based on the identified animal species.

The application discloses a kind of aquaculture water body in aquatic animal identification method, system, storage medium and equipment, it is related to image recognition technical field, the method is by camera module deployed in water body interior and is gathered multi-view image construction image set, and by floating type communication device synchronization space-time metadata stable transmission to cloud end;In cloud end, sequentially execute the unsupervised screening based on image content features, color self-adaptive correction according to the optical characteristics of water body and the frequency domain enhancement processing combined with semantic guidance, form three-level preprocessing pipeline, significantly improve image quality;Finally, the optimized image is input into pre-trained underwater target detection model, and the high-precision aquatic animal identification result is output.The method effectively solves the problems of poor data transmission stability, low target feature recognition and insufficient real-time identification in complex water environment.

The invention provides a multi-species wild animal video tracking method based on deep learning, and belongs to the technical field of wild animal recognition. The problem that tracking task modeling is feature matching between a template and a current frame image is solved, a motion model does not need to be preset, and the method can more flexibly adapt to field scenes with irregular motion and complex environment. The system is mainly composed of a feature extraction and encoder module, a detection branch, a tracking branch, an ID matching and updating module, a trajectory reconstruction module, a tracking trajectory compensation detection mechanism, a triple association strategy and a trajectory unification strategy, by adopting the tracking method based on time sequence correlation modeling, the interference problem of a complex environment and an animal movement mode in wild animal tracking can be effectively solved, and the method is more suitable for target tracking in a field dynamic scene.

This invention relates to the field of animal management technology and discloses an AI-based animal identification and management method based on unmanned aerial vehicles (UAVs). The method includes: responding to the generation of a heat map of suspected animal locations, a second UAV cluster performs a pinpoint confirmation task on the heat map area, collecting detailed image data of the animals; inputting the detailed image data into an animal identification model; based on species category and behavioral state, calling the corresponding animal movement prediction model from a model library and combining it with real-time environmental data to predict the animal's movement trajectory within a future time window; and controlling a third UAV cluster to generate a virtual electronic fence according to the predicted movement trajectory and a user-defined range. The method in this invention not only enables rapid animal species identification and population statistics but also generates a virtual electronic fence based on the predicted animal movement trajectory and a user-defined range, comprehensively considering both animal activity trends and human management needs.

This application relates to a vehicle risk avoidance method and electronic device, belonging to the field of vehicle technology. The vehicle risk avoidance method includes: when the vehicle is parked and an animal recognition event is detected, acquiring animal status information and personnel location information, wherein the animal recognition event indicates the presence of a dangerous animal target within a preset area around the vehicle, and the animal status information is determined based on the state of the dangerous animal target; the personnel location information is used to represent the relative position between the personnel associated with the vehicle and the vehicle; determining a target risk avoidance strategy based on the animal status information and personnel location information; and controlling the vehicle to execute the target risk avoidance strategy. This application establishes a complete risk avoidance process from risk perception to targeted risk avoidance strategy formulation, and then to automatic execution of the risk avoidance strategy, enabling vehicles to provide users with proactive warnings and risk avoidance assistance in outdoor scenarios such as camping, thereby reducing the safety risks posed by dangerous animals in outdoor environments.

1. The name of this design product: Animal identification tag weighing and reconstruction file all-in-one machine. 2. Purpose of this design product: used for animal identification marking, weighing and filing. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: Stereoscopic drawing 1.

The present disclosure relates to an animal monitoring system (100) for an animal cage, the animal monitoring system (100) comprising: a physical platform divided into a grid of cells (106) wherein each cell (106) comprises at least one weighing sensor (108); an animal identification system; a processing unit configured to: identify at least one animal on the physical platform using the animal identification system; continuously obtaining a load signal from the load cell (108); the identified position and / or change in position of the at least one animal on the physical platform is tracked based on the load signal from the weighing sensor (108). The present disclosure also relates to a method of investigating an animal in an animal cage.

The invention discloses an animal identification and positioning method and system based on sound events, and relates to the technical field of biological acoustic monitoring, and the method comprises the steps: collecting multiple paths of original audio data to form a target sound event set; and performing animal type identification processing on the set, extracting and fusing acoustic features of each channel of audio, and generating an animal type determination result. Meanwhile, sound source space positioning processing is executed, and the estimated space position of the animal sound source is calculated based on the sound wave arrival time difference and the intensity difference. And performing data fusion and association on the type judgment result and the estimated position to generate an animal activity record containing the animal type and the position. And performing space-time continuity analysis on the record, performing association matching with historical animal activity records, and correcting or updating position or category information in the record. And finally, according to the animal activity record, generating an animal activity thermodynamic diagram or an animal trajectory diagram of the monitoring area. According to the method, the accuracy and data continuity of animal monitoring in a complex environment are improved.

A hand-held applicator for applying an identification tag to an animal, comprising a body (10), a first handle (11), a second handle (13), a first jaw (141), a second jaw (142), a tag loading area, a tag application area (101), a magazine for holding a plurality of interconnected animal tags (18), a tag loading device for loading the interconnected animal tags from the magazine into the applicator body, a tag cutting and pushing element (50) for separating a single animal tag from the plurality of interconnected animal tags and moving the single animal tag from the tag loading area to the tag application area, and an application assembly. Moving the tag cutting and pushing element (50) forwards and backwards and moving the first handle (11) and second handle (13) together causes the application assembly to apply the single animal tag to an animal.

A method for animal identification and tracking includes, during a dark cycle: illuminating an interior of an animal cage with light outside of a visible light range; and capturing a first image of the interior of the animal cage. The method also includes: detecting a first identification tag in the first image; extracting a first set of tag features from the first image; identifying a first animal identifier of a first animal, tagged with the first identification tag, based on the first set of tag features; interpreting a first animal position of the first animal occupying the animal cage based on the first image; appending a cage log with the first animal identifier, a first timestamp corresponding to a time of capture of the first image, and the first animal position.

A milking system (100) is designed to extract milk from an animal (101). The system (100) includes several components for efficient milk sampling and biomarker detection. It is characterized by a feed cassette (140) holding multiple vials (150), at least one of which is prepared for biomarker detection. During milking, a milk sampler (300) transfers some milk into the selected vial (150). The system also has an animal identification device (116) for identifying the animal (101) and an information tagging device (370) for tagging the vials with relevant details. A controller (120) is connected to the identification device (116), the milk sampler (300), and the information tagging device (370). The system identifies the animal, determines whether biomarker detection is required, and, if necessary, signals the milk sampler to transfer milk into the appropriate vial and signals the information tagging device to tag the vial with the animal's identity. The system streamlines the process of collecting and analyzing milk samples.

The present invention provides a method and system for identifying wild animals and counting species populations based on image processing captured by unmanned aerial vehicles (UAVs), and relates to the technical field of wild animal identification. The present invention significantly improves the accuracy of identification and statistics by fusing image recognition and sound recognition results and dynamically adjusting the shooting parameters of the UAV. By combining the YOLOv5 model, SORT algorithm, deep learning model and short-time Fourier transform for image recognition and sound recognition, and combining the image and sound recognition results, false positives and false negatives are effectively reduced, and the consistency and reliability of the data are improved. In addition, the shooting parameters of the UAV can be dynamically adjusted to find a balance between regional resolution and coverage, ensuring that the system can work effectively in various environments. This not only improves the recognition ability and data quality of the system, but also enhances the robustness and versatility of the system, thereby achieving the effect of improving the application effect and practical value of UAVs in wildlife monitoring.

The invention relates to the technical field of safety production in the civil explosive industry, in particular to a civil explosive industry production site animal identification and driving method and a patrol robot, and the method comprises the steps: obtaining the image information of a to-be-identified animal, the image information comprises the features of the to-be-identified animal and the position coordinates of the to-be-identified animal; according to the image information, obtaining to-be-identified animal types and corresponding animal position information in the production site; determining a driving strategy according to the type of the to-be-identified animal, and determining a driving route according to the animal position information; and driving the to-be-identified animal out of the production site according to the driving strategy and the driving route. The patrol robot provided by the invention can identify and position animals in a patrol area, formulate a driving strategy and a driving route, and dynamically drive foreign animals away. And the problem of potential safety hazards caused by foreign animals to the production area of the civil explosive industry is effectively solved.

A animal husbandry system, where a group of animals can move about freely in a shed, a plurality of sensors being provided, suitable for monitoring respective different regions of the shed, the sensors being provided in such a way that, collectively, they are suitable for monitoring substantially the complete shed. The animal identification system provided is suitable for identifying and locating individual animals in the shed, and includes processing means operatively connected to the sensors and to the animal identification system, the processing means being programmed for determining for each individual animal a urinating and / or defecating pattern or frequency. In this way, a highly useful system is realized. Firstly, in order to be able to monitor the health status of animals, urinating and / or defecating patterns or frequencies are highly meaningful. Secondly, they render an efficient cleaning of the shed possible.

The invention relates to the technical field of contour detection and recognition, and discloses a human and animal recognition method and system based on contour detection, and the method comprises the steps: executing a contour rectangle fitting strategy, fitting an external rectangle of an object according to an object point set, and outputting the geometric features of the object; executing a contour axial change detection strategy, calculating a main shaft of an external rectangle, and calculating a change value of geometric features in real time; executing an adjoint relation detection strategy, calculating displacement vectors of the human body point set and the object point set, and identifying an object owner of the object based on a set ratio threshold value; executing a surrounding-main back-and-forth motion judgment strategy, and detecting a displacement vector of the object point set to judge a motion mode; executing an active cooperative motion judgment strategy, and detecting displacement vectors of the object point set and the human body point set to judge a motion mode; and a holding judgment strategy is executed, the motion mode that the object point set is attached to the human body point set is recognized, whether the object is a pet or not is detected, the vehicle recognition capacity of the elevator is improved, and the misjudgment risk is reduced.

A small animal trap (100) comprising a trap housing (120) with at least one trap opening (121); at least one trap element (130) arranged within the trap housing (120), wherein the at least one trap element (130) is accessible via the at least one trap opening (121); at least one first identification device (150) for providing a first small animal identification signal; a second identification device (140) for providing a second small animal identification signal; wherein the at least one first identification device (150) and the second identification device (140) are different from one another; a control unit (170) for identifying, through the first small animal identification signal and the second small animal identification signal, whether the at least one trap element (130) has caught a small animal, and for providing a trap status signal;and at least one energy source (180) for providing energy to the at least one second identification device (140) and the control unit (170);

A system (100), and method (200) thereof, is provided for identifying and forecasting animal activity. The method (200) may comprise receiving (202) the images (112) from the camera (110) to define image specific raw data (114); assimilating (204) the image specific raw data (114) with contemporaneous weather data (140); determining (206) a mean rate of animal sightings for each of a plurality of prior time intervals associated with the assimilated image specific raw data using a first analytics model; minimizing (208) a difference between observed sightings and predicted sightings based at least in part on outputs from the first analytics model using a second analytics model; and determining (212) an amount of likely animal sightings for each of a plurality of future time intervals based at least in part on the outputs from the first analytics model and outputs from the second analytics model using a third analytics model.

One variation of a system includes a tag including a panel defining an inner face; an outer face opposite the inner face and comprising a digital optical identifier; and a set of perforations interposed between the digital optical identifier and a perimeter of the panel; a tag housing molded around the perimeter of the panel and through the set of perforations in the panel; defining a body supporting the inner face of the panel; defining a rim extending from the body, encasing an edge of the panel; extending through the set of perforations in the panel; and extending over the set of perforations in the panel; and a barb extending from the body opposite the rim; a backing plate configured to receive the barb of the tag housing; and an applicator configured to drive the barb through an ear of an animal and into the backing plate.

Presented is an animal identification tag (1) with a male component (2) and a female component (3) placed on either side of an animal's ear. These components are joined to secure the tag (1) to the ear. The male component (2) has a first panel part (20) and a pin (21), while the female component (3) has a second panel part (30) and a receiving part (31) for the pin. The first (20) and second (30) panel parts have distal and proximal ends. The pin (21) extends through the ear's piercing hole, and the receiving part (31) cooperates with the pin. At least one distal end has an extended part (22, 32) to prevent the corresponding panel part from passing into the ear's piercing hole.

Small animal trap (100) comprising a first trap (111) and a second trap (112); a trap housing (120) wherein the first and the second trap (111, 112) are arranged on the trap housing (120), in particular on opposite end sections (120a) of the trap housing (120); at least one identification device (130) for providing a small animal identification signal based on an identification parameter; a power source compartment (140) arranged on the trap housing (120), in particular in a central section (120b) of the housing (120), between the first and the second trap (111, 112); a control unit (150) configured to process the small animal identification signal and to determine whether the first and / or the second trap (111, 112) has caught a small animal, and to provide a trap status signal based on this determination; a base plate (122) which is attached to an underside of the trap housing (120);wherein the control unit (140) and / or the at least one identification device (130) are provided in a space between the base plate (122) and the underside of the housing (120); and a cover (121) coupled to a top of the trap housing (120).

The invention discloses a dynamic dome camera automatic tracking animal shooting system and method based on an AI edge. The system comprises an edge AI visual perception module, an edge decision and tracking control module and a dynamic dome camera execution module. The data storage and communication module is used for collecting video streams in real time by integrating a high-performance AI chip and a camera; a lightweight animal identification model is constructed and operated at the edge end, animal information is identified, when a target animal is detected, dome camera control information is calculated, a control instruction is generated, a control instruction of an edge decision and tracking control module is received, corresponding control operation of a dome camera holder and a camera is executed, local storage is carried out on edge equipment, and the target animal is identified. And information is transmitted to the cloud server through the low-power-consumption communication sub-module. According to the scheme, an automatic tracking animal shooting method on the edge side is provided, and a millisecond-level closed-loop control process from sensing to execution is realized.

The present invention relates to the technical field of animal image recognition, and is a multi-target recognition method and system based on animal image features, comprising: obtaining an animal image to be recognized and a target animal ID set, obtaining a standard animal image set based on the target animal ID set, identifying a group of images of the same family of animals in the standard animal image set based on the target animal ID, performing balanced grayscale conversion on the images of the same family of animals to obtain a grayscale animal image, obtaining a target contour feature value based on the grayscale animal image, and obtaining a target grayscale feature value group, performing key value pairing on the target animal ID, the target contour feature value, and the target grayscale feature value group to obtain a target animal feature group set, obtaining a target animal recognition model based on the target animal feature group set, and obtaining multi-target animal images using the target animal recognition model. The present invention can improve the accuracy of multi-target recognition of animal images and reduce the excessive consumption of time and human resources.

A system (24) for assigning an unassigned RTLS tag (6) to an animal identity, represented by an RFID tag (4) on a specific animal individual (2'). Real-time location data is obtained from a real-time location system / RTLS (18). Animal identification data is obtained from an animal identification system (14) comprising a first RFID reader device (10). The RTLS (18) covers a monitored area (22) including the first RFID reader device (10). A control arrangement (26) is configured to obtain a first timestamp associated with a reading of the RFID tag (4) by the first RFID reader device (10), obtain a second timestamp associated with the unassigned RTLS (18) locating the unassigned RTLS tag (6) in a first predefined area (40) associated with the first RFID reader device, and establish a correlation for assigning the unassigned RTLS tag (6) to the animal identity if a predetermined relationship between the first timestamp and the second timestamp is fulfilled, and assign the unassigned RTLS tag (6) to the animal identity of the specific animal individual (2') based on the established correlation.

The present invention provides a method and device for identifying wild animals. The method extracts features from an image to be processed to obtain feature maps of multiple scales. After selecting several feature maps to be activated, key point prediction is performed on the feature maps of multiple scales based on a key point prediction layer to obtain key point heat maps under corresponding multiple channels. The feature map to be activated and the key point heat maps under multiple channels are then fused based on a key point representation layer to obtain key point representations of each key point. The key point representations of multiple key points in the image to be identified are then matched pairwise with the key point representations of multiple key points in a first sample image corresponding to a specific wild animal species to obtain a key point matching result of the image to be identified. Based on the key point matching result, it can be determined whether the species of the animal in the image to be identified is the specific wild animal species, thereby overcoming the interference caused by different animal motion states and improving the accuracy of wild animal identification.