127 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.

The invention relates to the technical field of image recognition, and discloses a laboratory animal identity recognition method and system based on image recognition, and the method comprises the steps: carrying out the preprocessing of an animal motion image sequence collected by a high-speed camera in a laboratory, and obtaining the image data of a target animal; performing two-dimensional discrete Fourier transform and inverse Fourier transform to obtain animal image high-frequency feature data and animal image low-frequency feature data; respectively inputting into a double-branch diffusion network for feature processing to obtain fused animal feature data; constructing a positive sample pair, constructing a negative sample pair through difficult sample mining, calculating a feature distance based on cosine similarity and performing momentum updating to obtain an animal identity feature template; and based on the double-branch diffusion network and the animal identity feature template, feature extraction and similarity calculation are performed on the real-time animal image sequence to obtain animal identity data, so that the distinguishing capability of identity features is improved, and the time sequence consistency of an identity recognition result 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 discloses an endangered animal identification system and method based on a local comparison algorithm and a related device, and relates to the technical field of endangered animal identification, the system comprises a database which is a domestic database edited by experts, and a visual platform module receives a to-be-detected local mitochondrial DNA sequence of a to-be-detected endangered animal input by a user, the local comparison module adopts a local comparison algorithm to locally compare the to-be-detected local mitochondrial DNA sequence with a plurality of reference local mitochondrial DNA sequences in a database to obtain a local comparison result, and determines species of the to-be-detected endangered animal based on the local comparison result; and the visual platform module also displays the species of the to-be-detected endangered animal and detailed information of the species of the to-be-detected endangered animal. According to the application, the comparison efficiency and the comparison precision of the DNA sequence can be improved, and a domestic database is adopted, so that the data security and reliability are improved.

The invention provides a wild animal identification and counting method and device based on a multi-source unmanned aerial vehicle video, and the method comprises the steps: planning a flight route of an unmanned aerial vehicle based on a delimited detection region, and setting a flight height; a wide-angle visible light image and an infrared image are collected through the unmanned aerial vehicle; unifying the sizes of the wide-angle visible light image and the infrared image; inputting the wide-angle visible light image and the infrared image which are unified in size into a target detection network, and obtaining an identification result output by the target detection network; and tracking and counting the identified target wild animals based on a ByteTrack target tracking algorithm. According to the wild animal identification and counting method and device based on the multi-source unmanned aerial vehicle video, the wide-angle visible light image and the infrared image are acquired through the unmanned aerial vehicle, detection and tracking are performed by using the visible light image data and the infrared image data, and the detection and tracking precision is improved through data fusion; and the efficiency and the accuracy of identifying and counting the wild animals are improved.

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.

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.

The utility model discloses a multi-color combined splicable ear tag, which belongs to the technical field of animal husbandry management and animal identification, when the multi-color combined splicable ear tag is used, a male tag and a female tag are respectively positioned on two sides of an animal ear, and a puncture part of an ear stud on the female tag is fixed in an ear stud fastening hole of the female tag after penetrating through the animal ear. The male tag and the female tag are arranged on the two sides of the animal ear respectively, the ear tag pieces with the same number and the same color combination can be installed on the male tag and the female tag in the circumferential direction, ear tag information on the animal ear can be conveniently and rapidly recognized from the distance, the two sides of the ear and all angles in the circumferential direction, and the problem that the view is shielded and cannot be observed is solved.

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.

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.

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.

Disclosed is a method for providing animal identification information using a blockchain-based non-fungible token (NFT) performed by a computing device The method for providing animal identification information may comprise the steps of: uploading, to a predetermined first blockchain network, information indicating whether an electronic registration associated with identification information for a target animal has been issued; uploading, to a predetermined second network, verification information for a first NFT generated by being added to the first blockchain network and the identification information for the target animal; and transmitting, to a user terminal, a recognition code including access information for the second network.

A female part of an animal identification device. The female part includes a head having an inner receiving cavity that can receive a tip of a male part of the identification device. The female part includes at least one element for fixing an accessory to an outer face of the head. The at least one fixing element is in the form of at least one groove.

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 invention relates to an animal identification and / or monitoring device applicator (1) comprising two jaws (13, 14) intended to receive an animal ear to be tagged comprising a female and a male component (31, 32), said male component comprising a tip and said female component comprising a through-hole (321) for receiving the tip. The applicator comprises mechanism for securing an element to one of said jaws, said securing mechanism comprising elastic return means (42, 52) and a mobile retaining part (41, 51) for holding the female before assembly, said second jaw having an internal space (141) in which the securing mechanism is mounted, and said retaining part being movably between: • - a securing position in which the retaining part secures the female component on said second jaw, and • - a release position in which the retaining part is pressed so as to release the female component from said second jaw.

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.

The invention discloses an endangered animal recognition feedback system and method based on image recognition and a related device, and relates to the technical field of endangered animal recognized.The system comprises a man-machine interaction module, an image recognition module and a knowledge base module, and the man-machine interaction module is used for receiving a to-be-detected image, input by a user, of a to-be-detected endangered animal; the image recognition module is used for performing image recognition on the to-be-detected image and determining species of the to-be-detected endangered animal, and the man-machine interaction module is further used for calling detailed information of the species of the to-be-detected endangered animal from the knowledge base module based on the species of the to-be-detected endangered animal. And displaying the species of the to-be-detected endangered animal and detailed information of the species of the to-be-detected endangered animal, wherein the detailed information comprises pictures, names, protection levels, morphological characteristics, common transaction categories, common illegal utilization forms, values and distribution places. The method can efficiently and accurately complete the recognition and feedback of the endangered animal based on image recognition, and is timely and reliable.

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.