35 results about "Background subtraction" patented technology

The embodiment of the invention provides an actor recognition method, system and device based on ViT gait analysis and a medium, and belongs to the technical field of gait recognition. The method comprises the following steps: acquiring behavior video data of a target object; performing background subtraction processing on the behavior video data to obtain a binary image of the gait of the target object; inputting the binary image into a feature analysis model, wherein feature analysis is constructed and formed based on Transform; performing attitude feature extraction through a double-path parallel multi-head attention module of the feature analysis model, fitting a feature weight of the attitude feature through a feature average fusion module, and outputting an embedded feature vector; and comparing the embedded feature vector with a preset feature vector, and identifying the identity of the target object according to a comparison result. The embodiment of the invention aims to improve the accuracy of performing actor identity recognition through gaits.

The invention relates to the technical field of environment monitoring, in particular to an internet-of-things-based breeding environment monitoring system for pond turtles, and the system comprises a turtle movement trajectory tracking module which is used for collecting real-time images of an incubator, comparing pixel differences among continuous image frames to carry out background subtraction, and recognizing a moving foreground target. According to the method, the pixel differences between image frames are continuously compared, the geometric center point of the moving target is calculated, continuous recording of the moving tracks of the water tortoise individuals in the cultivation box is achieved, the moving area is gridded, the occurrence probability is calculated by counting the frequency that coordinates fall into all units, an objective data basis is provided for environment optimization, and the method is suitable for large-scale popularization and application. Meanwhile, multiple groups of temperature sensors are arranged at key positions such as a spawning area and a drying platform, multiple paths of environment sensing redundant numerical values are obtained, the state range is predicted according to a water body thermal inertia model, multiple paths of readings are updated and calibrated, deviation data are effectively recognized and eliminated, and the reliability of environment monitoring data and the fault tolerance of the system are improved.

The invention discloses a fan non-stop routing inspection blade tip triggering method, which comprises the following steps: S1, an unmanned aerial vehicle executes a fixed route to reach a blade tip waypoint, a single-point laser radar is adopted to measure and calculate the spatial pose of a fan blade in real time, whether the unmanned aerial vehicle triggers photographing within a preset time is judged, and if not, the unmanned aerial vehicle stops routing inspection; if so, calling a leaf tip perception compensation algorithm to calculate a new leaf tip photographing waypoint; s2, through a blade tip sensing compensation algorithm, realizing stable tracking and picture locking of the pan-tilt camera on the blade tip of the fan, and obtaining an image of the fan at a horizontal position through a background difference method; s3, calculating the proportion of the blade in the picture, and calculating the distance from the current waypoint position to the blade tip in the horizontal direction by combining the internal reference of the pan-tilt camera and the inspection distance; and S4, the unmanned aerial vehicle is controlled to fly to the new blade tip waypoint, and blade image capturing is executed. According to the scheme provided by the invention, a closed-loop mechanism is formed through the single-point radar, the compensation algorithm, the background difference and the waypoint offset calculation process, and accurate acquisition of the blade tip area image is completed for accurately calculating the target point again.

Image sensor, which includes the following: a pixel array including multiple pixels configured and functional to detect light from a scene, wherein the scene includes an object to be detected and a background image, wherein each pixel of the multiple pixels is configured to generate a new voltage signal in response to the intensity of light received by the pixel; a memory for storing digital values ​​corresponding to a predetermined voltage bandwidth around a prior voltage signal R of at least some of the multiple pixels corresponding to the light detected from the background image, wherein the voltage bandwidth defines a positive delta voltage +Δ and a negative delta voltage -Δ relative to the background voltage signal R; a sample-and-hold or track-and-hold module capable of sampling the new voltage signal corresponding to one of the several pixels selected; a digital-to-analog converter that generates a reference voltage R+Δ or R-Δ from the digital values ​​stored in the memory; a comparator that receives the new voltage signal sample value from the hold module and receives one of the reference voltages R+Δ or R-Δ for the selected pixel to compare it with the voltage signal sample value, wherein the comparator is configured to produce a high-voltage output signal if the voltage signal sample value is greater than the reference voltages R+Δ or R-Δ, and to produce a low- or zero-voltage output if the voltage signal sample value is less than the reference voltages R+Δ or R-Δ; a two-bit register containing a most significant bit (MSB) and a least significant bit (LSB), wherein the two-bit register provides a two-bit output signal; and a logic module that receives the voltage output from the comparator, wherein the logic module is configured and functional as follows: Selecting the reference voltage R+Δ to be supplied to the comparator, and setting the MSB to a logic "0" in response to a low-voltage output signal from the comparator or to a logic "1" in response to a high-voltage output signal from the comparator; and then Selecting the reference voltage R-Δ to be supplied to the comparator and setting the LSB to a logic "0" in response to a low-voltage output signal from the comparator or to a logic "1" in response to a high-voltage output signal from the comparator; a background subtraction module configured and functional to receive the two-bit output signal from the two-bit register for the selected pixel and, based on the two-bit output signal, to identify the selected pixel as detecting light from a scene that is different from the light detected by the same pixel in the background image; and a processing module for generating an output image that includes the selected pixel identified by the background subtraction module.

The invention discloses a fire fighting access occupation detection method based on multi-target tracking. According to the method, firstly, foreground targets are extracted through background subtraction and morphological processing, and then an independent life cycle management mechanism is established for each target by adopting a multi-target tracking algorithm based on intersection-to-parallel ratio IOU matching. By calculating the continuous existence time of the target and combining the spatial relationship between the target position and the preset no-parking area, the intelligent judgment that the alarm is triggered only when the continuous parking of the target in the area exceeds the set threshold value is realized. According to the method, the problem that the false alarm rate of a traditional area intrusion detection system is high is effectively solved, instant passing and illegal occupation behaviors can be accurately distinguished, and the accuracy and practicability of fire fighting access occupation detection are remarkably improved.

This invention discloses an adaptive adjustment method and system for a virtual environment, relating to the field of virtual reality technology. The method includes: performing background subtraction processing on a target image sequence when a user is experiencing a virtual environment; extracting detailed features from the target image sequence, and identifying key points using target anchor boxes based on the detailed feature information; determining the coordinates of three-dimensional key points based on the target key point information for user action behavior analysis; extracting a user eye image sequence from the target image sequence to determine eyeball image information, and analyzing the gaze direction based on the eyeball image information; analyzing the user's attention focus using image brightness distribution based on the gaze direction information; and adjusting elements of the virtual environment based on the user's action behavior information and user attention focus information. This invention enables more fine-grained virtual environment adjustment, improving the quality and naturalness of the user's immersive experience in the virtual environment.

The invention discloses a fixed scene real-time target counting system based on example driving, and the system comprises a cross-modal interaction module which receives a visual frame selection example and a natural language instruction to generate a unified example feature vector, and a lightweight feature extraction module which extracts a fixed scene image multi-scale feature map based on a post-distillation lightweight DINO backbone network. The space-time correlation processing module filters static interference and focuses on a dynamic target through an optical flow method and a background subtraction algorithm by utilizing the background constant characteristic of a fixed scene, and the real-time density map generation module normalizes a strategy for a priori optimization density map; and the edge computing optimization module performs edge device optimization deployment to reduce the computing power consumption and the storage demand of the embedded device. The method has the beneficial effects that multi-modal input processing, lightweight efficient feature extraction, accurate space-time target tracking, real-time density map generation counting and low-power optimization of edge equipment are realized, and the real-time performance, the accuracy and the deployment flexibility of the system are improved.

The invention discloses an inland waterway traffic complexity evaluation method based on a radar sequence image, belongs to the technical field of intelligent monitoring of inland waterways, and aims to solve the problems of poor traffic situation perception real-time performance, low ship detection precision and insufficient dynamic interaction quantification in the prior art. Radar noise is removed through background subtraction and a gray region threshold value, a target and a background are separated, and accurate ship detection is achieved; kalman filtering and a Hungary algorithm are adopted to construct a continuous motion trail, an Euclidean distance cost function is utilized to distribute identification numbers, and the problem of number jump in shielding and dense scenes is solved; integrating relative distance, encounter angle and trend dynamic information, and quantifying ship interaction strength through a nonlinear function; and constructing an undirected weighted graph model, and realizing traffic complexity visual evaluation based on the symmetry of the adjacent matrix. According to the method, high-risk ship pairs can be marked in real time, accurate data support is provided for collision avoidance decision making and channel scheduling, and channel traffic management efficiency and safety are remarkably improved.

The present application belongs to the field of computer vision, and particularly relates to a video anomaly detection method, system and device based on scene classification. The steps of the present application method are as follows: step S1, performing gray processing on original video frames, using background subtraction method and frame difference method to extract foreground targets, and extracting pixel features through Double-Canny algorithm; step S2, using a pre-trained model to detect the number of people in the video frames and generate a heat map of the video frames; step S3, using K-means clustering algorithm to classify scenes of the video frames, and dividing the video frames into two categories of dense scenes and sparse scenes; and step S4, using an anomaly detection module to respectively perform anomaly detection on the dense scenes and the sparse scenes. The present application can select different network structures for anomaly detection according to scenes with different crowd densities, is better applied to complex real scenes, and achieves better anomaly behavior detection effect.

This invention discloses a visualization measurement method for solid-liquid phase change of electrically controlled solid propellants based on image recognition. The method is implemented using an electrode clamping device and includes synchronous control of image acquisition and laser illumination, background subtraction combined with adaptive threshold binarization, application of regional constraints and morphological image processing, extraction and tracking of phase change interface features, and statistical analysis of target parameters. This method enables continuous and intuitive optical observation of the solid-liquid phase change process, achieving high-fidelity visualization of the phase change process before combustion. Through the synergistic effect of laser directional illumination and image recognition algorithms, it effectively distinguishes the solid region, liquid molten region, and suspended particle region of the propellant. Candidate motion regions are segmented using background subtraction combined with adaptive threshold binarization, reducing light interference caused by laser illumination. This achieves effective identification and interface extraction of the main solid-liquid phase change region and weak phase change region, improving the accuracy of phase change morphology characterization.

The invention discloses a machine vision-based sundry road occupation detection method. The method comprises the following steps of: acquiring and preprocessing data; dual-mode differential detection: running a template differential channel and a background subtraction channel in parallel, calculating an absolute difference value between a current frame and a reference template by the template differential channel, and performing binarization processing and morphological closed operation to obtain a newly added static target area; the background subtraction channel dynamically models a background based on an MOG2 algorithm and separates a moving target; removing all pixels overlapped with the dynamic target mask in the newly added static target area through matrix subtraction operation, and outputting a static abnormal target image; target analysis; target tracking; and decision output. According to the embodiment, high accuracy can be achieved in various environments, the method can effectively distinguish temporary articles from illegal lane occupation, real-time alarm is supported, an efficient solution is provided for intelligent security and protection, and the method is suitable for intelligent supervision requirements of factories, fire fighting accesses and other scenes.

In an FMCW radar system, the chirp pair modes of the master and slave radar units are selected such that when one chirp in the master radar's chirp pair (A,B) coincides in time with one chirp in the slave radar's chirp pair (C,D), the other chirs in these pairs do not coincide in time. This allows for background subtraction and enables the slave radar to achieve self-synchronization with the master radar by detecting the in-band tone in the difference between the spectral responses obtained when the slave radar chirps and the master radar signals are mixed, and driving it to a specific setpoint after detecting the tone.

This invention provides a mobile object detection device and a mobile object detection program that efficiently detect moving objects using the background subtraction method. [Solution] The moving object detection device 200 comprises an object detection unit 250 and a background update unit 290. The object detection unit 250 detects an object estimation region represented in the input data 610 based on difference data 620 representing the difference between background data 600 and input data 610. The background update unit 290 updates the background data 600 based on fixed object data representing the object when background conditions are met, including the fact that the velocity of the object represented in the object estimation region is below a threshold. The difference data 620 is used to detect the moving object 20 represented in the input data 610.

The application provides a Mach-Zehnder interference structure free-state molecule interaction analysis method and system, in a free system, a light intensity imaging module is used to perform molecule interaction monitoring, through a Mach-Zehnder interference optical path, a refractive index change caused by free system biomolecule combination is converted into a phase difference between two different polarization components formed by a detection light at a detection arm and a reference arm; subsequently, a weak value amplification effect is introduced by near-orthogonal post-selection, so that the small phase difference is converted into a light intensity change, and system noise interference is reduced through light intensity image processing, baseline correction and background subtraction; the application can solve the problems of spatial steric hindrance, limited mass transfer, complex detection process, difficulty in truly reflecting the molecule interaction process in the free system, insufficient sensitivity to weak signals, and easy environmental noise interference in the existing fixed system label-free detection technology, and can realize high-sensitivity, real-time and high-throughput detection of biomolecule interaction in a free solution system.

The present disclosure provides a particle size statistical method of granular minerals of shale, comprising: collecting shale samples; cutting the collected samples into the size of 1 cm×1 cm×1 cm; performing argon ion polishing; magnifying the samples by the field emission scanning electron microscopy, collecting 500-1000 images; using Adobe Photoshop to seamlessly splice the collected images; using ImageJ in combination with EDS energy spectrum to determine types of mineral particles, performing background subtraction, and setting a reasonable grayscale threshold to classify the particle minerals; performing binary processing on the images of selected mineral particle types; identifying edges of granular minerals, and using a Analyze Particles command to statistically analyze and measure particle sizes, and drawing a particle size distribution histogram. The method can perform precise measurement and statistics on the particle sizes of shale particle minerals, and can perform full-scale statistical analysis on the distribution of different mineral particle sizes in the shale.

The invention belongs to the technical field of radiation detection, and particularly relates to a synthetic aperture radiometer target detection method and device under a high dynamic condition based on continuous frame image processing. Utilizing a synthetic aperture radiometer to continuously obtain millimeter wave signals of a target scene, and obtaining visibility functions of a plurality of continuous frames according to preset integral time; performing background subtraction, Gaussian low-pass filtering and Fourier transform on the visibility function sequence to obtain a plurality of frames of brightness temperature maps; solving a rotation matrix of the current frame image according to the attitude angle at the current moment, and performing registration fusion on the brightness temperature image according to the rotation matrix to obtain a preliminary result image; a convolution kernel is set according to antenna array arrangement and platform motion imaging properties, the preliminary result image is enhanced, then the image is further enhanced by adopting multi-scale white top-hat transform, and a final result image is obtained; and setting a target detection window, screening candidate target points according to pixels in the window, and finally setting a threshold value according to a full-image pixel mean value and variance to determine the positions of the target points.

Disclosed are systems and methods that leverage machine learning techniques to provide information about items that are on display during a live event. A 3D volumetric capture system is used to construct a 3D representation of the event in real time. For example, depth and color data from multiple perspectives may be used to construct a 3D mesh of colored polygons that represent the scene. Users may view the live event with a 3D enabled virtual reality or augmented reality headset. Background subtraction may be used to isolate items of interest. In some configurations a machine learning model may process the 3D mesh to generate a textual description of items in the scene. The textual description may then be used to search for more information about a particular item. Additionally, or alternatively, the machine learning model may directly predict one or more items in the scene.

The invention discloses an intelligent garbage classification method based on multi-modal sensing and AI recognition, and the method comprises the steps: obtaining the information of a person approaching equipment, carrying out the real-time analysis through a lightweight neural network model, and recognizing and distinguishing a real user from an environment disrupter; continuously comparing the difference between the current scene image frame and the reference scene by adopting a background difference algorithm, judging whether a release behavior exists or not, and awakening the gravity sensor to enter a standby state when the effective release behavior is judged; collecting image data of the thrown object; collecting weight data of the thrown object; collecting spectral data of the thrown object; inputting the three types of modal data into a D-S evidence theory model and outputting a weighted probability distribution vector containing all garbage categories; a three-level confidence policy is adopted to execute delivery, and a data packet containing twelve-dimensional features is generated in each decision. According to the invention, the operation efficiency of the whole garbage classification and recovery is improved, the daily average handling capacity reaches 800 times, the throwing response time is shortened to 0.5 second, and the number of clearance vehicles is reduced by 40%.

The invention relates to the technical field of quality detection of agricultural and livestock products, in particular to an ImageJ-based rapid nondestructive measurement method for the content of fat in pig muscle, which comprises the following steps: acquiring a fascia-removed pig longissimus dorsi sample image, and eliminating surface specular reflection interference by spraying methylene blue or loading a polarization component during acquisition. And constructing a background estimation model based on a rolling ball algorithm to execute background subtraction, and performing nonlinear stretching on a color channel to enhance the contrast of fat and muscle. The corrected image is converted to an HSB color space, pixels are screened through a three-dimensional threshold interval to generate a preliminary fat mask, and non-fat noisy points are removed by removing small-area independent connected domains; meanwhile, a sample region-of-interest mask is generated using edge detection or threshold segmentation. And the intramuscular fat content is obtained by calculating the ratio of the fat pixels to the sample pixels. Reflection and background noise interference are effectively eliminated, and objective, accurate and automatic measurement of the fat content in the pig muscle is achieved.

This invention discloses a vision-based intelligent sorting system and method for multi-joint robotic arms. It replaces binocular stereo matching with monocular vision and motion prediction, eliminating the need for a PLC intermediate layer to construct a native control architecture for the robotic arm. A layered software architecture comprising a vision processing layer, a motion planning layer, and a control execution layer achieves a closed-loop process from perception to decision-making to execution. Vision processing integrates background subtraction and optical flow methods to achieve dynamic target tracking and pose calculation. Motion planning, based on transmission linear velocity and visual positioning data, predicts the grasping point and generates a smooth trajectory. The control execution layer employs an adaptive fuzzy PID algorithm to dynamically adjust parameters, eliminating mechanical vibration and improving trajectory tracking accuracy. This invention effectively solves the problems of visual processing delay, insufficient dynamic tracking accuracy, and poor environmental adaptability in traditional sorting systems, and can be widely applied in industrial scenarios such as electronic component sorting, logistics parcel sorting, and waste material recycling.

The invention discloses an unmanned aerial vehicle accurate landing control system based on industrial vision, and relates to the technical field of unmanned aerial vehicle visual navigation and control, and the system comprises the steps: collecting a landing field image sequence through an optical industrial camera array carried on an unmanned aerial vehicle, and carrying out the dynamic background subtraction and multi-channel image registration through an image preprocessing module; and generating a fused differential image which highlights the specific optical identification. The feature recognition module performs morphological skeleton extraction and angular point group detection on the image, acquires sub-pixel-level coordinates of identification feature points, and forms a real-time image feature point set. And the pose resolving module carries out distortion correction on the point set according to camera calibration parameters, and solves real-time pose parameters of the unmanned aerial vehicle relative to the identifier by using a three-dimensional space geometric model of the identifier through a perspective point resolving algorithm. The control instruction module generates a landing control instruction according to the parameters. According to the method, the robustness and the pose calculation precision of visual landing in a complex environment are improved.

The invention relates to the technical field of image processing, in particular to a lining paper production quality detection method and system based on image processing, and the method comprises the steps: obtaining an image of lining paper, and carrying out the preprocessing of the image to obtain a preprocessed image; obtaining a strip line feature map formed by response values of the pixel points, wherein the response values reflect the consistency and continuity strength of the gradient direction of the pixel; and carrying out two-dimensional Fourier transform on the standard image. According to the method, by introducing the path integral of the gradient direction consistency weight, the directional periodic gradient analysis based on Fourier transform and the weight template and the normalized adaptive background subtraction, the quality problem defect characteristics of strip lines, pressure leakage, white exposure and the like are respectively and accurately extracted. And then, the pre-trained neural network is utilized to carry out adaptive weighted fusion on the multi-dimensional features, a unified defect saliency map is generated, and the accuracy and robustness of carrying out multi-type defect detection on a complex surface are remarkably improved.

The application provides a multi-view gait recognition method based on twin MViT, and relates to the technical field of gait recognition. The multi-view gait recognition method based on twin MViT comprises the following processing steps: S1, a data preprocessing step, a background subtraction method is used to extract human contour features on a CASIAB multi-view gait dataset, then a large amount of useless black background information is cropped, and the gait information of the collector is displayed in the center; S2, a design idea of a twin neural network is used. By using a view conversion tensor, a multi-angle gait feature conversion method is used, starting from the design of a twin neural network, a simple view feature conversion method is designed, a high-precision set and a low-precision set are respectively transmitted into a constructed twin angle feature extraction module, and the feature tensor is regarded as a conversion result of the low-precision set feature to the high-precision feature on the premise of retaining the main features of the low-precision set, so that the accuracy is improved.

The invention provides a non-contact evaluation method for a health state of a calf in a lactation period, which comprises a multi-spectral imaging unit, a depth sensing unit and a high-sensitivity directional audio acquisition unit which are deployed above a living area of the calf, synchronously and continuously collecting a non-contact multi-mode original data flow of the target calf in a preset monitoring period; and transmitting the multi-modal original data stream to an edge computing node for timestamp alignment and data preprocessing, wherein preprocessing comprises illumination normalization and background subtraction on image data to highlight a calf target. Through non-contact fusion application of multispectral, depth perception and directional audio acquisition technologies, the system can effectively avoid stress response caused by operations such as capturing and fixation under the condition that natural behaviors of the calves are not disturbed at all, so that a welfare environment which is more beneficial to growth is created for the calves, and the survival rate of the calves is improved. And meanwhile, the collected data can better reflect the real state, and a solid foundation is laid for accurate evaluation.

This invention discloses an automatic peak-finding method for X-ray diffraction (XRD) spectra, relating to the field of phase identification based on XRD spectra. The method mainly includes the following steps: S1: Constructing wide and narrow window symmetrical zero-area transformation functions to transform the spectral data and form a transformation result group; S2: Comparison method and peak flattening processing; S3: Background subtraction, intensity screening, and width margin calculation; S4: Data fusion of the transformation results, using the "AND" fusion criterion to screen out channels not within the peak position characteristic range; S5: Cluster analysis to further divide peak positions into peak and non-peak positions, obtaining the final peak position determination result. Using the automatic peak-finding method proposed in this invention for XRD spectrum peak finding eliminates the need for manual parameter setting, accurately identifying spectral peak positions. This solves the problem of traditional peak-finding methods requiring frequent parameter adjustments based on input spectral data characteristics, thus limiting their application and bringing convenience to phase identification.

Method comprising: Receiving a current vehicle image representing a current interior of a vehicle (100); Detecting, by means of an occupancy management system (104), of at least one passenger (F) in the vehicle (100) based on the current vehicle image, further comprising: Accessing an image of an empty vehicle (100) representing the interior of the vehicle (100) without passengers (F); and Subtracting the image of the empty vehicle (100) from the current vehicle image using a background subtraction algorithm, wherein the background subtraction algorithm is one of the following: Weighted Moving Mean, Adaptive Background Learning, Fuzzy Gaussian, Gaussian Mixture Model, and Multi-Layer BGS; Determining, by means of the occupancy management system (104), a seat (S) of the passenger (F);Generating, through the occupancy management system (104), a seat card that identifies the seat (S) of the passenger (F) in the vehicle (100); and estimating the age, weight, height, gender, and emotion of the passenger (F).

The invention relates to the technical field of intelligent traffic monitoring, in particular to a lightweight edge end video scene offset monitoring method based on dynamic and static separation, which comprises the following steps of: accessing a monitoring video secondary code stream through edge equipment, extracting frames and converting the frames into a grey-scale map; background subtraction is carried out by adopting a Gaussian mixture model, a static background region is extracted in combination with morphological operation, and dynamic object interference is shielded; feature points are extracted and screened in a lightweight manner in a static region by using an AKAZE algorithm; high-robustness inter-frame matching is realized through violent matching, a Love ratio test and an RANSAC (Random Sample Consensus) algorithm; calculating a homography matrix based on the matching points, and resolving a rotation angle, a translation amount and a matching confidence coefficient in a camera plane; dynamically adjusting a judgment threshold according to the confidence coefficient, and judging scene offset in combination with continuity check; and if the deviation occurs, recording detailed information and uploading an alarm.

The present application relates to the field of health care information technology, and in particular to a primary liver cancer postoperative micro-metastasis detection method based on multispectral molecular imaging. A high-frequency echo proportion map is constructed by means of an undetected radio frequency signal, and a liver parenchyma region is segmented to exclude scar interference; a pulsation amplitude map is extracted by means of a multispectral photoacoustic time sequence image, and the phase is corrected in speed by means of the high-frequency echo proportion map to eliminate speed distortion caused by the scar; background subtraction is performed based on the average drift trend of the liver parenchyma region to generate a net signal enhancement map; a negative correlation linear model of the pulsation amplitude and the net signal is fitted in the liver parenchyma, and the residual is calculated to represent metabolic clearance abnormalities; the residual and the corrected phase map are weighted and fused to generate a supply and demand imbalance index, and accordingly, an abnormal region is adaptively identified. The method effectively decouples structural artifacts and abnormal regions, and significantly improves detection specificity and positioning accuracy.

The application provides a defect identification method based on optimization of industrial image quality, and relates to the technical field of industrial image quality optimization; the application respectively performs contrast enhancement processing on whether the ROI region type on the industrial image is linear, then applies a multi-scale detail enhancement method to make the ROI region details of the ray image after contrast enhancement more obvious, finally combines a threshold adaptive binarization method to perform background subtraction to obtain potential abnormal regions on the measured workpiece image, and performs labeling, so as to assist in improving the artificial inspection efficiency.

This invention discloses a mouse scratching analysis method based on image data processing, belonging to the field of computer processing technology. The method includes the following steps: S01, acquiring behavioral videos of mice using at least two simultaneous cameras with different perspectives, performing ultra-lightweight localization of the mouse using a background subtraction algorithm, calculating the minimum bounding rectangle of the mouse body as the region of interest, and performing hardware-level region of interest cropping on the camera sensors based on the minimum bounding rectangle, storing and transmitting only the cropped region of interest image. This invention effectively filters out non-scratching behaviors such as walking and jumping by using a coarse-grained determination of the relatively fixed torso and a pause recognition mechanism for unstable states; combined with hind paw-neck distance fluctuation cycle analysis, scratching analysis, and cyclic high-intensity marking, it achieves fine quantification from scratching rounds to single scratching fluctuations, significantly reducing the false alarm rate and improving the purity and richness of behavior recognition.