79 results about "Image differencing" patented technology

The invention provides a door and window glass defect feedback control method and device, electronic equipment and a storage medium, and relates to the technical field of door and window glass defect detection. The method comprises the steps of collecting a first image after a polarized light imaging detection device is adjusted to suppress reflected light on the surface of glass and enhance scattered light of bubbles in the glass; after the polarized light imaging detection device is adjusted to enhance reflected light on the glass surface, a second image is collected; calculating an adaptive weight group by analyzing local characteristics of the second image; weighting the first image according to the self-adaptive weight group, and subtracting the weighted first image from the second image to obtain a difference image; and extracting bubble defect information in the glass by analyzing the differential image. According to the method, interference caused by non-uniformity of glass surface reflection is overcome, the adaptability of image differential processing to surface reflection background suppression is improved, and therefore image features of internal bubble defects are extracted more effectively and more accurately.

The invention relates to the technical field of image data processing, and discloses an element mounting quality detection method for a chip mounter, which comprises the following steps of: synchronously acquiring images before and after element mounting and a vacuum pressure signal of a mounting suction nozzle, compensating vibration by utilizing an inherent substrate background area in the images, obtaining an optical position deviation through image difference and mass center calculation, and calculating the surface mounting quality of the element according to the optical position deviation. And finally, logically associating the deviation with the change characteristic of the pressure signal, and classifying the physical process of the mounting event. According to the invention, through the collaborative diagnosis of optical detection and physical signals, the physical reason of the mounting event can be clearly distinguished; the inherent limitation caused by vibration interference and unclear fault attribution during high-speed operation of a traditional detection mode is avoided, and the method has good adaptability to incoming material elements with different appearances.

The invention relates to a projector automatic obstacle avoidance method and device combining adaptive picture cutting and deep learning, and the method comprises the steps: enabling a projector to project a white background picture, capturing a projection picture through a camera, calculating a difference image based on a projection region background image and a projection picture image, carrying out the adaptive cutting of the difference image, and obtaining a projection region image, the difference image can highlight the difference between the projection area and the background area, the projection area image can be found out more conveniently, then a deep learning model is used for detecting an obstacle in the cut image and recording position information, the maximum projection area is calculated, the position information of an obstacle-free area is obtained and then converted to a projector coordinate system, and the position information of the obstacle-free area is obtained. And finally projecting a final picture. Therefore, the automatic obstacle avoidance of the projector is realized, the clear and complete projection picture is ensured, the problem of poor projection effect in a complex environment due to traditional manual processing and the prior art can be effectively solved, and the projection quality of the projector in a complex scene is improved.

The invention relates to the technical field of defect detection, and particularly discloses a high-precision defect detection system based on image difference and threshold processing, which comprises a registration image acquisition module, a fixed mask conversion module, an image difference detection module, a correction difference graph generation module and a defect set output module, the method comprises the following steps: firstly, completing affine registration by shape features, changing a fixed mask into a dynamic mask, and performing edge robustness in the mask to obtain a preprocessed image; local movement difference is carried out on the preprocessed image and the multiple reference images, fusion of a reference domain and a time domain is carried out to generate a difference response image, and candidate areas are extracted in combination with a dynamic mask; estimating and correcting a displacement field in the candidate range, synchronously mapping to obtain a correction difference graph and a correction mask graph, and determining a peripheral rejection area; according to the method, false alarm and missing detection are effectively reduced, and the method is suitable for online high-precision detection.

The invention relates to the technical field of new energy power station monitoring, in particular to a new energy power station anomaly detection method and system based on acoustic imaging, and the method comprises the steps: obtaining the acoustic sequence information of a target new energy power station photovoltaic panel, and arranging a plurality of monitoring units based on the acoustic sequence information; transmitting sound waves with a preset frequency to the photovoltaic panel through a sound wave transmitter, collecting reflected sound wave data through an acoustic imaging sensor, and generating an acoustic image sequence; identifying and identifying anomalous reflection regions of the plurality of acoustic images based on the acoustic image sequence; carrying out image difference processing on the acoustic image sequence, and carrying out region detection on an acoustic difference image to obtain an abnormal region of the photovoltaic panel; and classifying the abnormal areas of the photovoltaic panel, and performing cross verification on the preliminary abnormal detection result by using abnormal reflection distribution to generate abnormal distribution of the photovoltaic panel. By emitting specific sound waves to the photovoltaic panel and analyzing the reflection characteristics of the sound waves, the structural change of the internal material of the photovoltaic panel can be sensed.

The invention relates to the technical field of mold detection, and discloses a special working state inner wall detection device for a neutral borosilicate molded bottle mold, which comprises a workbench, an opening and closing mechanism is arranged on the middle side of the top of the workbench, a clamping mechanism is arranged on the top of the workbench, and a six-degree-of-freedom mechanical arm is mounted on the rear side of the top of the workbench. A ceramic probe is arranged on the outer side of the six-degree-of-freedom mechanical arm, and a base fixing seat is fixedly connected to the outer side of the ceramic probe. A real-time image differential analysis module is arranged, a blowing and powder spraying mechanism is used for carrying out pneumatic excitation on a suspected defect area, and a multispectral endoscope module is controlled to respectively collect optical images before and after excitation; by calculating the normalized difference measure of the two images and comparing the normalized difference measure with the preset physical displacement threshold value, the non-solid-phase attachments displaced due to pneumatic excitation and the solid-phase defects with fixed positions can be distinguished, and the technical problem that two abnormal areas with different properties are difficult to effectively classify in the prior art is solved.

The invention relates to the technical field of image processing, in particular to a weld joint performance visual detection method and system based on image processing, and the method comprises the steps: for any weld joint, obtaining a gray level image of any weld joint in each sampling period, and obtaining a gray level image sequence; carrying out image difference processing on the grayscale image sequence to obtain a difference image sequence, and screening at least two target images with potential damage evolution regions from the difference image sequence; for any target image, acquiring a comprehensive damage coefficient of any target image according to the roughness degree and the texture feature of each potential damage evolution region in any target image and the confusion degree of pixel points in each potential damage evolution region; and the function relationship between the sampling period and the remaining service life of any welding seam is constructed by using the data change trend of the comprehensive damage coefficient of each target image, and the remaining service life of any welding seam is predicted, so that the fatigue damage state of the welding seam is predicted in advance.

A method, system, device and medium for detecting a dark space target and predicting a track under a dense fixed star background comprises the following steps: firstly, obtaining a reconstructed multi-pulse function by using a frequency spectrum phase difference, further estimating motion parameters of a dark star background and a space target, and then designing an image difference fusion coarse processing module to obtain a candidate target image sequence; the number of tracks is effectively reduced; and finally, according to the motion parameters and the candidate target image sequence, directional track association is carried out, an association area is reduced to a fan-shaped range, and redundant false alarms are removed while the target track is accurately and rapidly detected. The system, the equipment and the medium are used for realizing the invention. Based on the distribution characteristic of the space target and the time domain motion characteristic, the method not only achieves the precise suppression of fixed stars with different brightness, but also improves the detection robustness of targets with different star magnitudes, and has a better detection effect on dark space targets with different motion parameters under the background of dense fixed stars.

The invention discloses a routing inspection image feature detection and disease recognition method, which comprises the following steps: firstly, matching a routing inspection image acquired by an unmanned aerial vehicle with a template image in a pre-constructed image sample library by adopting a template matching algorithm, and determining an original template image corresponding to the routing inspection image; secondly, performing feature extraction on the inspection image and the matched template image by adopting an image feature extraction algorithm to obtain image features of the inspection image and the template image; and then, according to the extracted image features, adopting an image difference algorithm to obtain difference features between the inspection image and the template image, and obtaining a change feature image of the inspection image, thereby realizing intelligent identification and marking of a disease area in the inspection image, and finally, inputting the change feature image into a trained disease classification model, thereby realizing disease classification. And classifying and identifying diseases in the inspection image through the disease classification model.

The application discloses a method for intelligent identification of changes in river and lake water areas by a UAV and early warning of abnormal problems in rivers and lakes, comprising the following steps: constructing a digital twin bottom plate data of rivers and lakes; manually editing a basic flight route of the rivers and lakes, intelligently generating a shooting flight route based on the basic flight route and the digital twin bottom plate data of the rivers and lakes, splicing the basic flight route and the shooting flight route, and obtaining a dynamic flight route of the UAV; performing dynamic flight route flight according to the dynamic flight route, collecting data information during flight, and returning the data information to a UAV base station through a network and transmitting the data information to an edge computing device; generating river and lake orthographic images and river and lake spatial terrain data by combining the data information collected by the UAV with water area management lines; and identifying and comparing the orthographic images before and after the two times by using a water area surface identification model and an image difference method, so as to obtain a water area surface change area and abnormal problems in the rivers and lakes. The method solves the problems of low identification accuracy, low image spatial resolution and unintelligent flight route scheduling in the existing UAV patrol of the rivers and lakes.

The invention relates to the technical field of industrial robots, and discloses a robot flexible plug-in teaching method and system based on double-vision collaboration.The method comprises the steps that S1, a double-vision system and a robot coordinate system are calibrated, and a feeder area is defined; s2, in a teaching mode, a human hand track is tracked through a global positioning camera, and a'material taking 'action at a feeder is automatically identified by using a sliding window clustering algorithm; s3, triggering a high-precision Mark point camera to shoot a local image of the PCB after the human hand leaves the plug-in, and accurately calculating a plug-in position coordinate of the component through an image difference technology; and S4, automatically synthesizing a robot executable program according to the identified action sequence and the calculated accurate coordinates. According to the method, traditional manual programming is replaced with visual teaching, the operation technical threshold is remarkably reduced, the industrial-grade operation precision is guaranteed through a double-layer cooperation mechanism of hand guiding and visual fine calibration, the multi-variety production line changing time is greatly shortened, and flexible automatic plug-in is achieved.

The present invention relates to a method and device for automatic obstacle avoidance for a projector that combines adaptive cropping of images with deep learning. First, the projector projects a white background image, then uses a camera to capture the projected image. A differential image is calculated based on the background image of the projection area and the image of the projection screen. The differential image is adaptively cropped to obtain the projection area image. The differential image can highlight the difference between the projection area and the background area, making it easier to find the projection area image. A deep learning model is then used to detect obstacles in the cropped image and record their position information. The maximum projected area is calculated, and the position information of the obstacle-free area is obtained. This information is then converted to the projector coordinate system, and the final image is projected. This method achieves automatic obstacle avoidance for the projector, ensuring a clear and complete projection image. It can effectively solve the problem of poor projection effects in complex environments caused by traditional manual processing and existing technologies, and improve the projection quality of the projector in complex scenes.

The present invention discloses a method for detecting sea surface targets based on wake wave characteristics and a computer-readable medium. The present invention uses image differential processing to obtain differential detection images from multiple consecutive frames of images; performs target identification on each differential detection image frame and obtains a target determination result; repeatedly determines whether a target exists on all differential detection images, and if a target exists, performs target background cancellation to obtain a target background cancellation image; performs primary target identification on the target background cancellation image, and based on this, performs clustering, optimization, and pairing to obtain a secondary target identification image; estimates the target's heading based on the secondary target identification image; and obtains target heading information based on multiple frames of the secondary target identification image. The present invention can achieve effective detection of sea surface targets with a low false alarm rate, and is based on image domain processing, with the advantages of small computational data volume and high operational efficiency.

The present application relates to the technical field of visual detection device, especially to a mirror shift type double-sided visual detection device. Its technical scheme comprises a rack, a conveying component and a visual detection component. The conveying component adopts double-sided conveying belt support to make the middle part of the workpiece suspended, and the visual detection component realizes synchronous imaging of the top surface, bottom surface and side surface of the workpiece through upper and lower industrial cameras and mirror reflection. In view of the problem that the mirror surface is easy to be contaminated, leading to misjudgment, the device is provided with a movable protective film covering the mirror surface, and when the initial detection is unqualified, the protective film is driven to move and secondary shooting is performed. Through image difference intelligent discrimination of defects, real defects and stains on the film are effectively distinguished. The present application is also integrated with a spacing adjustment and automatic sorting mechanism, which can complete multi-angle and high-precision detection and sorting in a single process, and has the advantages of high detection efficiency, strong anti-interference ability, good adaptability and the like.

The invention discloses a printing defect detection method and detection device for a cylindrical bottle body, and relates to the technical field of image matching, and the method comprises the following steps: collecting a bottle body image in a rotating state; extracting positioning points by using a normalized cross correlation algorithm; abnormal points are screened out through linear regression, and image cutting is completed through affine transformation; selecting an optimal period image by adopting a perceptual hash algorithm; correcting the stretching distortion in the vertical direction through an improved thin-plate spline transformation model; and finally, carrying out image difference on a corrected image and a template image and identifying printing defects. The detection device using the detection method can automatically complete bottle body conveying, image acquisition and defect identification and rejection, and high-precision and automatic detection of printing defects of cylindrical bottle bodies of different specifications is realized.

The present invention discloses a printing defect detection method and system based on single-sample learning and skeleton reconstruction. A data augmentation scheme suitable for printing images is used to augment a single printing image, avoiding overfitting of the model to training samples. The image is reconstructed by the skeleton through the network, and the reconstruction task can effectively control the generalization ability of the model, enabling the model to accurately reconstruct the normal area while generating a large reconstruction error in the abnormal area. At the network design level, an improved denoising autoencoder structure based on dilated convolution and randomly copied and pasted noise is adopted to capture both global information and local details. An image comparison algorithm suitable for the printing field is used. Through image differencing and combined with opening and closing operations, the defect position is finally determined. Through experiments and engineering verification, the present invention can effectively detect various printing defects and has wide engineering application value.

The application is suitable for the field of machine vision and industrial detection technology, and provides a metal workpiece defect detection method and system based on image difference, which comprises the following steps: collecting a to-be-detected image and a normal reference image, and pre-processing the two images, then performing pixel-level absolute difference operation to generate a difference image; the to-be-detected image, the normal reference image and the difference image are spliced to form a multi-channel input tensor, which is then input into a lightweight convolutional neural network to output a defect probability heat map and determine a coarse positioning mask; the coarse positioning mask is subjected to connected domain analysis to filter noise and obtain a candidate defect region; the candidate defect region is subjected to fine segmentation to obtain an accurate defect mask; and the candidate defect region and the accurate defect mask are superimposed and displayed on the original to-be-detected image to obtain a detection result. The application combines the difference information of the normal reference image and the to-be-detected image, and introduces a fine segmentation network, thereby effectively improving the detection accuracy and robustness.

The invention relates to the technical field of image processing and defect detection, in particular to an image enhancement method for surface defects of a mining sputtering sensor, which comprises the following steps: an image mapping step: acquiring a single-exposure two-dimensional digital image; mapping a pixel gray value into a node temperature, and constructing a virtual enthalpy field matrix; a matrix evolution step: calculating a local structure tensor, extracting a texture main direction, and generating a heat conduction coefficient matrix; virtual time iterative evolution is started, and a local second derivative is calculated; a barrier construction step: constructing a heat insulation wall if a preset sudden change condition is met; otherwise, keeping unchanged; a decoding difference step: generating a steady-state thermal field and reversely decoding the steady-state thermal field into a substrate background image; performing differential operation to extract a heat dissipation residual image; a fusion output step: superposing the residual image to a non-highlight area to output an enhanced image; extracting or filtering a thermal gradient output defect topological coordinate system; according to the method, self-adaptive smoothing of highlight and forced sharpening of defect edges are realized.

The invention relates to the technical field of microlens detection, in particular to an automatic microlens detection method and system, and the method comprises the steps: obtaining a stripe image of a microlens under the illumination of a stripe structure; preprocessing the stripe image to obtain a preprocessed image with a block area as a foreground; determining a rotation angle according to the boundary of the reference block region, and rotating the corrected stripe image to align the stripe with the row direction; segmenting the rotating image, identifying a connected block, solving an external frame to generate a block mask and a complementary set interval mask thereof, and obtaining a block image and an interval image; performing segmentation on the block image, taking a median value according to rows to replace and generate a block reference image, performing differential binarization to obtain a block defect candidate, and performing independent binarization and merging on an abnormal region; overlapping the interval image and the block mask, performing closed operation to suppress line noise, and then performing binarization inversion to obtain an interval defect candidate; and superposing the two candidate output defect mark images. According to the invention, the defect can be automatically identified and marked and output by the microlens under the stripe background.

The present application provides a seat positioning estimation device, a seat positioning estimation method, and a seat positioning estimation computer program. The seat positioning estimation device has: a masking image generation section (31) that generates a masking image by masking a region representing a driver in an image representing the driver in a vehicle cabin generated by an imaging section (2) during a period in which the driver is seated in the vehicle (10); a difference image generation section (32) that generates one or more difference images by differencing each of one or more reference images that differ from each other in positioning of a driver's seat (11) and the masking image; and an estimation section (33) that inputs each of the one or more difference images to an identifier, obtains an individual estimation value of the positioning of the driver's seat for each difference image, and calculates a statistical representative value of the individual estimation values obtained for each difference image as an estimation value of the positioning of the driver's seat.

The present invention discloses a method, system, and computer-readable storage medium for detecting and extracting linear defects in LCD screens. The method comprises: S1. acquiring an image to be inspected and performing ROI image cropping on the image to extract the actual area to be inspected in the image; S2. Gaussian filtering the obtained ROI image using filter kernels of different sizes to obtain a foreground image and a background image of the ROI image; S3. performing image difference on the foreground image and the background image to obtain a foreground-background separation image; S4. performing Canny edge detection on the foreground-background separation image to obtain a Canny detection image with the defects outlined; S5. performing a progressive probabilistic Hough line transform on the Canny detection image to obtain linear defect information; and S6. re-clustering the extracted defect information to obtain accurate defect information. This invention improves the accuracy and efficiency of linear defect detection in screens.

The invention discloses a faint target multi-frame enhancement method based on differential comparison. The method is suitable for a multi-faint target image sequence obtained by an infrared detector, and comprises the following steps: step S101, obtaining an infrared faint target image sequence; step S102, background suppression of the infrared image sequence; step S103, carrying out segmentation and multi-frame accumulation on the front and back sequence image groups; step S104, performing contrast enhancement processing on the local features of the front and rear images; step S105, front and back sequence accumulation enhanced image difference contrast factor extraction; and S106, calculating a target difference comparison multi-frame enhancement result. For a dark target image sequence obtained by an infrared detection system, target energy is remarkably enhanced by adopting multi-frame accumulation and local contrast enhancement, the signal-to-noise ratio of the target is improved by combining the local space contrast difference of a difference result after the movement target is enhanced, dark targets with different movement states in a field of view can be accurately enhanced, and the detection accuracy is improved. And an efficient and reliable technical scheme for enhancing and detecting the faint target is provided for the field of infrared detection.

This application relates to the field of steel pipe production defect identification technology, specifically providing a visual inspection-based steel pipe production defect identification management method and system. The method includes the following steps: S1, simultaneously acquiring low-frequency and high-frequency structured light pattern images of the steel pipe surface; S2, performing distortion correction and noise filtering on the low-frequency structured light pattern image to obtain a preprocessed low-frequency image; S3, adjusting the enhancement parameters of the high-frequency structured light pattern image based on the preprocessed low-frequency image to obtain an enhanced high-frequency image; S4, performing image difference operations on the enhanced high-frequency image and the preprocessed low-frequency image to obtain a difference image; S5, using preset defect discrimination rules or a defect classifier to identify steel pipe production defects in the difference image. This method can overcome the interference of water film on steel pipe production defect identification without relying on an additional water film removal step.

This application discloses a method for collaborative evaluation of the physical modal segmentation of seedling stems and leaves and their physiological quality, belonging to the field of agricultural robot perception and precision detection. The method includes the following steps: outputting a horizontal sinusoidal excitation signal and acquiring at least two images of the seedling under two morphological conditions within the leaf resonant frequency range; performing image difference on any two images to obtain a grayscale difference map; extracting a dynamic mask of the leaves from the grayscale difference map; segmenting the leaves and stems in one of the two images based on the dynamic mask; determining the measured amplitude of the leaves based on the dynamic mask; determining the physiological stiffness of the leaves based on the measured amplitude; determining the stiffness difference between the physiological stiffness and a stiffness threshold, and determining the ratio of the stiffness difference to the stiffness threshold; and determining the seedling quality based on the ratio. This application improves the accuracy and robustness of stem and leaf recognition and segmentation, and meets the requirements for early non-destructive recognition under low computational power and water stress.

This invention discloses a machine vision-based method for measuring the trajectory of an electromagnetic needle selector. The method includes: acquiring a continuous image sequence of the needle selector during operation using a high-speed industrial camera; adaptively cropping the Region of Interest (ROI) using sparse optical flow fusion and image differencing; performing Otsu thresholding, connected component extraction, minimum bounding rectangle fitting, and gray-level centroid calculation on each needle selector region within the ROI to extract feature points of multiple needle selectors; constructing an inter-frame feature matching and trajectory reconstruction algorithm to continuously identify and sort the temporal trajectory of each needle selector; and finally outputting a complete and smooth multi-target motion trajectory. Compared to traditional single-point measurement methods, this method has advantages such as non-contact operation, high concurrency, and high precision, and is suitable for applications such as electromagnetic needle selector status assessment, fault detection, and performance analysis.

The invention discloses a dairy product surface mold quantity automatic detection method and device based on image recognition, and relates to the technical field of image processing, and the method comprises the following steps: obtaining a polarization difference image and an original RGB image in a detection period, the polarization difference image being generated by the difference of two images with parallel and vertical polarization directions; inputting the polarization difference image into a main path of the two-path domain adaptive network to obtain a suspected mould region segmentation result; intercepting a corresponding area from the original RGB image, inputting side road classification, and obtaining a mould area confirmation result; according to the method, reflection is suppressed through polarization difference imaging, the generalization of trans-dairy product type detection is improved by combining the two-way domain self-adaptive network, accurate recognition and quantification of the mold are achieved, and the method can be applied to the field of dairy product quality detection.

The invention provides equipment for actively detecting a short-wave infrared aiming mirror, and relates to the field of photoelectric detection. The equipment comprises a short-wave infrared observation module, a detection resolving module, a main control module and a display module, wherein the short-wave infrared observation module is used for acquiring an image of an environment area; the detection resolving module comprises a target resolving unit and a laser detector, the laser detector is used for emitting detection laser, capturing the detection laser returned by a target object in an environment area and capturing position information of the returned detection laser, and enhancing the returned detection laser through signal processing; the target resolving unit is used for determining a display screen coordinate corresponding to the target object based on the first image and the second image collected by the short-wave infrared observation module through an image difference mode and a threshold value analysis mode, and sending the display screen coordinate to the main control module; and the main control module is used for framing the coordinates of the display screen and controlling the display module to display the environment area, the target object and the framing.

The invention relates to the technical field of steel pipe production defect recognition, and particularly provides a steel pipe production defect recognition management method and system based on visual inspection, and the method comprises the steps: S1, synchronously obtaining a low-frequency structured light pattern image and a high-frequency structured light pattern image of the surface of a steel pipe; s2, performing distortion correction and noise filtering processing on the low-frequency structured light pattern image to obtain a preprocessed low-frequency image; s3, adjusting enhancement parameters of the high-frequency structured light pattern image according to the preprocessed low-frequency image to obtain an enhanced high-frequency image; s4, performing image difference operation on the enhanced high-frequency image and the preprocessed low-frequency image to obtain a difference image; s5, performing steel pipe production defect identification on the difference image by using a preset defect discrimination rule or a defect classifier; according to the method, the interference of the water film on steel pipe production defect identification can be overcome on the premise of not depending on an additional water film removal step.

The invention relates to the technical field of intelligent storage recognition, and particularly discloses an intelligent storage adaptive recognition system based on deep learning. According to the method, multi-source goods storage identification data are fused, a defect area is positioned and a goods defect value is identified through processing such as image difference, the goods sealing performance is judged by using a DTW algorithm, factors such as a storage environment are integrated to predict an actual residual period of efficacy of goods, and a goods storage state discrimination vector is constructed according to the goods defect value, the sealing performance and the actual residual period of efficacy. The cumulative probability of the goods storage state grade is calculated through a multivariate ordered Logistic regression model, the category with the maximum probability is selected as the storage state grade, and intelligent judgment of the goods storage state is achieved; meanwhile, corresponding strategies are executed based on different goods identification results, intelligence and automation of goods warehouse management are achieved, and the overall efficiency and reliability of warehouse operation are improved.

The invention discloses an image change detection method and device, electronic equipment and a storage medium. Acquiring a multi-temporal to-be-detected image of the target earth surface area; carrying out differential processing on the multi-temporal to-be-detected image to obtain a candidate change region, and carrying out feature extraction on the multi-temporal to-be-detected image to obtain local features and global features; performing display weighting on the local features and the global features according to the candidate change regions to obtain region enhancement features; performing feature fusion and decoding processing on the region enhancement features to obtain a pixel-level change probability graph consistent with the resolution of the multi-temporal to-be-detected image; and determining the change condition of the target earth surface area according to the pixel-level change probability graph. According to the technical scheme, the image change detection efficiency and detection precision are improved.