640 results about "Texture feature" patented technology

The invention relates to the technical field of computer vision, in particular to a defect segmentation positioning method and system for an inorganic mineral casting image, and the method comprises the following steps: calling an illumination image to analyze brightness, matching exposure parameters, splicing the image, analyzing a gradient, recognizing a defect, screening an effective region, calculating a gray variance, and constructing roughness weight recognition texture features. According to the method, the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined, the exposure interval can be dynamically adjusted when the casting image is processed, the defect type information is output in the direction, and the positioning information is generated by correcting the recognition position in combination with the actual coordinate of the target spot. The method has the advantages that the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined; the method has the advantages that the method is simple and easy to implement, detail loss of overexposure areas is reduced, the recognition precision of defect areas is improved, accurate area segmentation and classification processing are achieved, roughness weight calculation combining gray variance and pixel density is combined, the sensitivity to surface fine defects is enhanced, and the precision and reliability of defect positioning are improved.

The invention discloses an image multi-modal feature extraction method, a ground feature classification and recognition method and a GIS image generation method. Comprising the following steps: firstly, extracting texture features from a target image by adopting a multi-directional statistical method fusing rotation invariant coding of a local binary pattern and a gray-level co-occurrence matrix, and extracting color features from the target image by adopting an LAB-HSV dual-color space collaborative analysis method to obtain features of different modes of the target image; and then according to the information values of the extracted color features and texture features, adjusting the weight ratio corresponding to the color features and the texture features so as to optimize the recognition precision of the classification model on complex ground features. And finally, according to a weight ratio corresponding to the color feature and the texture feature, performing weighted fusion on the color feature and the texture feature to obtain a corresponding multi-modal feature vector.

The invention belongs to the technical field of hydraulic engineering detection, and particularly provides a hydraulic metal structure surface coating defect detection method and a hydraulic metal structure surface coating defect detection device based on image processing. In a wet and dry alternating environment of a hydraulic metal structure, a multispectral imaging device is used for acquiring an image of a metal structure surface coating, and the acquired multispectral image is preprocessed; a complete and clear image to be detected is obtained; extracting features related to coating defects from the preprocessed to-be-detected image, wherein the features comprise spectral features, texture features and color features; and inputting the extracted features related to the coating defects into a pre-trained classification model, and classifying the coating defects through the classification model. According to the method and the device, the state of the coating can be comprehensively described, and the type and the degree of the coating defect can be accurately identified.

The invention relates to the technical field of image segmentation, in particular to an automatic segmentation method and system for an echocardiogram of the department of cardiology, and the method comprises the following steps: based on image data of the echocardiogram, extracting gray level distribution, edge feature and texture feature information, analyzing gray level change amplitude, screening gray level change abnormal regions, and recognizing connectivity features. According to the method, the segmentation accuracy is effectively improved by extracting image gray, edge and texture features and identifying abnormal regions, noise and artifact interference are reduced by optimizing low connectivity regions, and the segmentation accuracy is improved. A problem area is analyzed and positioned in combination with multi-frame gray level change, a segmentation result is adjusted, the processing stability and consistency are enhanced, meanwhile, an optimized alarm node is output based on a frequency trend, more accurate and stable heart image analysis is supported, and the clinical application practicability is improved.

The invention belongs to the technical field of image processing, and discloses a nonferrous metal target detection method and system for a scraped car. According to the method, through deep coupling of a GAN bimodal fusion network and a double-backbone network, a scraped car nonferrous metal target detection model is constructed, so that nonferrous metals in scraped cars are efficiently and accurately sorted. A bimodal fusion network is introduced into a model input layer, a fusion image with infrared thermal saliency and visible light texture features is generated, and the problem of cross-modal information splitting is solved; according to the dual-backbone network, standard convolution is replaced by a three-branch structure of an MGHCM module, large target contours, middle target semantics and small target details are synchronously captured, and self-adaptive shunting and efficient fusion of multi-scale features are achieved in cooperation with dynamic feature routing of a DHFBlock module; and the detection head network is optimized into a rotating frame prediction and cross-modal cross entropy classification mechanism so as to adapt to the placement scene of the metal fragments at any angle.

The invention discloses a quality detection system based on three-dimensional point cloud scanning and RGB image fusion, relates to the technical field of industrial automatic detection, and aims to solve the problem of high quality detection error rate caused by the fact that a detection result is easily interfered by visual angle change and illumination conditions in an existing detection method. According to the method, the color point cloud model under the unified coordinate system is constructed through space calibration and point pixel level registration, and the structural information and texture features of the workpiece are effectively reserved. Therefore, the problem that the quality detection error rate is high due to the fact that the detected object has curvature dramatic change, a strong reflection area or frequent shielding is solved. According to the method, the detection accuracy is improved, meanwhile, the robustness and the operation efficiency are high, the industrial workpiece detection requirements with complex curved surface structures and high surface quality requirements can be met, and the actual application requirements of an industrial site for an intelligent surface detection system are met.

The invention relates to the technical field of image recognition, in particular to a burn scar hyperplasia risk assessment method combining semantic segmentation and texture feature analysis, which comprises the following steps: acquiring an image gray gradient, color jump and texture variance, calculating pixel mutation, extracting a mutation boundary, constructing a periodic direction field, and extracting a continuous offset region. According to the method, by extracting the pixel gray gradient, the color jump and the texture variance, calculating the boundary sudden change intensity and generating the layer, the structure change characteristics can be refined, the image anomaly perception precision can be enhanced, the semantic boundary can be identified based on the sudden change sequence, and the physical continuity is prevented from interfering the segmentation accuracy. A periodic evolution record is constructed through direction gradient, the dynamic trend of the structure is disclosed, an expansion area is locked in combination with direction continuous offset and change stability, a classification label is constructed through point location density, direction consistency and a gradient module value, and the interpretability and accuracy of risk identification are improved.

The invention mainly relates to the technical field of tunnel engineering, and provides a tunnel surrounding rock grading dynamic correction method based on image texture features in order to improve the accuracy and real-time performance of surrounding rock grading under complex geological conditions. An improved deep residual network model integrated with an attention mechanism is input and introduced, the model improves the precision of single-point surrounding rock grade identification based on key textures, an initial prediction value of the surrounding rock grade is obtained, and meanwhile a time sequence prediction model is introduced; the whole excavation process is regarded as a continuous geological sequence based on the multi-source surrounding rock feature vectors of the current excavation cycle and at least one previous historical cycle, a time sequence trend value of the surrounding rock grade is obtained, a self-adaptive weight is set, the surrounding rock grade initial prediction value and the time sequence trend value of the surrounding rock grade are smoothed and corrected, and the surrounding rock grade is obtained. Therefore, the accuracy and the stability of the surrounding rock grading result are improved.

The invention discloses a method for estimating plant biomass based on map multi-modal feature extraction and fusion. The method comprises the following steps: acquiring a plant RGB image and a multi-spectral image; inputting the preprocessed RGB image and NIR wave band image into a double-model cooperation segmentation framework to realize image segmentation; binary image features, color features, texture features, reflectivity and the like are calculated, feature splicing is carried out, and high-dimensional features are constructed; carrying out dimension reduction on the high-dimensional features; and training a deep neural network through the effective features and the biomass to realize biomass estimation. According to the method, a zero sample learning-based double-model cooperation segmentation framework is utilized to realize accurate segmentation of a single plant on the premise that a large number of training sets are not needed; multi-modal feature information is extracted based on the segmented single plant image, an improved SHAP model is introduced to reduce the feature space dimension, and the inversion precision and the operation efficiency are improved while the information effectiveness is ensured; through a high-precision deep neural network model, rapid, lossless and accurate biomass acquisition is realized.

The invention discloses a cloth surface flaw detection method and device based on texture perception and anomaly detection. The method comprises the following steps: acquiring a surface image of detected cloth; inputting the surface image of the detected cloth into a deep learning network model; a multi-scale feature map is extracted through the backbone network; processing the feature map through the texture perception feature extraction module so as to fuse cross-channel and cross-space texture information; integrating anomaly detection branches through the check network, and outputting feature maps of different scales; performing frequency domain enhancement and spatial domain feature extraction operation and fusion on the features through the adaptive frequency domain convolution module; and outputting a detection result through the YoloHead detection head so as to judge whether the cloth has flaws or not. According to the method, the texture feature information in the cloth image can be effectively utilized, and the detection accuracy and robustness of the cloth surface flaws and the recognition capability of unknown flaws are improved.

The invention relates to the technical field of image recognition, in particular to a shearing behavior dynamic correction method and system based on wear state recognition. Acquiring a digital image sequence of the cutting edge area; constructing an image feature separation network, and performing parallel feature extraction on the preprocessed digital image sequence; identifying a pixel-level high-frequency texture discontinuous region and an edge gradient direction field by utilizing continuity characteristics of a cutting edge surface periodic texture mode to obtain a target defect probability graph; identifying a projection shadow area and a low-frequency illumination halation of the edge of the bulge by using a backlighting imaging model to obtain an interference artifact probability graph; establishing spatial mutual exclusion constraints of the target defect probability graph and the interference artifact probability graph in a pixel space, and generating a defect binary mask; performing multi-dimensional texture feature calculation on an area corresponding to the defect binary mask, and constructing a surface state feature vector; according to the invention, based on the surface state feature vector, the defect mode category is discriminated, and the corresponding shearing correction parameter is generated.

The invention belongs to the technical field of image processing, and particularly relates to a real-time image monitoring method and system for a yucca extract separation process, and the method comprises the steps: obtaining a separation process diagram, and obtaining illumination unevenness according to the brightness of pixel points; determining a self-adaptive filtering size according to the illumination unevenness, and performing enhancement processing on the image to obtain an enhanced image; obtaining a separation tendency index according to the local difference and the texture feature of the enhanced image; segmenting the image according to the separation tendency index to obtain a connected region, and obtaining a solid-phase aggregation degree according to the morphological characteristics of the connected region; and finally, judging a separation end point according to the change trend of the solid-phase aggregation degree and the regional area variance in the time window. According to the method, the problem of non-uniform illumination of an industrial site is solved through the adaptive enhancement algorithm, the separation end point is accurately judged in combination with morphological characteristics, and the monitoring accuracy and robustness are improved.

The invention relates to the technical field of health assessment, in particular to a traditional Chinese medicine health assessment method and system based on intelligent analysis, and the method comprises the steps: carrying out the multispectral image collection of the face and tongue picture of a user, carrying out the color gradation quantification processing and texture feature extraction of the obtained image data, and obtaining a face color gamut feature matrix and a tongue picture texture feature vector; and based on the tongue picture-complexion associated features and a syndrome description text input by the user, performing intelligent classification pre-judgment on traditional Chinese medicine syndrome types to obtain an initial syndrome label set, and based on user physique data, correcting the initial syndrome label set to obtain a syndrome feature fusion vector. Through the health fluctuation index and the health trend early warning signal, the change of the health condition of the user can be dynamically evaluated, and a personalized health intervention scheme is generated on the basis, so that the fluctuation of the health condition can be reflected in real time, intervention measures can be taken in time, and potential health risks can be prevented.

The invention relates to the technical field of image processing, in particular to a concrete member surface defect detection method and system based on image segmentation, and the method comprises the steps: obtaining a surface image of a concrete member, and dividing the surface image into a plurality of image blocks; and performing frequency domain transformation on any image block to obtain a power spectrum. According to the method, the feature space period of each image block is analyzed and calculated through frequency domain transformation, and adaptive weighted fusion is carried out on texture features at different distances by using Gaussian weight on the basis of the feature space period. According to the method, the feature extraction process can dynamically adapt to the physical scale of image local textures, namely, small distance analysis is automatically emphasized on fine textures and large distance analysis is automatically emphasized on rough defects, so that scale-perceived composite texture features are constructed; and the accuracy of identifying the concrete surface defects under the complex texture background is obviously improved.

The invention discloses a reversible information hiding method and system for enhancing image smoothness, and relates to the technical field of information security, and the system comprises a preprocessing module, an information embedding module and an optimization recovery module. According to the method, the Sobel operator is utilized to calculate the image gradient map, the region is divided based on the threshold T, the smooth region and the texture region can be accurately distinguished, an accurate basis is provided for differential processing of different regions, region characteristics are fitted, large-size blocks and sub-blocks of the smooth region are divided to facilitate subsequent refinement processing based on the gradient mean value, and the processing efficiency is improved. The small-size blocks of the texture area are beneficial to operation aiming at texture features, the rationality and effectiveness of overall processing are improved, meanwhile, the pixel mean value in the large-size blocks of the smooth area is calculated, the pixel and mean value difference value is recorded, reversible low-pass filtering is carried out, and the reversibility of the image in the whole information hiding and recovering process is ensured.

The invention belongs to the technical field of coating defect detection, and provides a terahertz image reconstruction method and a terahertz image reconstruction system for coating internal defects, which can enhance global features and image texture consistency features of images in a low-resolution data set through non-local feature enhancement and cross-scale fusion processing. Obtaining second multi-scale feature information corresponding to the image; through hole space pyramid pooling processing, channel attention enhancement, space attention enhancement and multi-scale feature fusion processing, the edge contour features and texture features of the image can be improved while space position information of a defect area is reserved, and third multi-scale feature information corresponding to the image is obtained; and the second multi-scale feature information and the third multi-scale feature information are fused to enhance the features of the edge contour and texture details of the whole region of the reconstructed terahertz image, so that the detection precision of the method for detecting the internal defects of the coating based on the reconstructed terahertz image can be improved.

The invention relates to a double-spectrum fused intelligent monitoring imaging method and system, and the method comprises the following steps: firstly, synchronously collecting a visible light image and an infrared image of a monitoring scene, and calibrating the pixel coordinates to generate an aligned double-spectrum map; then, extracting the texture features of the visible light image and the temperature gradient of the infrared image from the aligned bispectrogram, and respectively obtaining a texture feature map and a temperature gradient map; feature validity judgment is carried out based on the two images, a bispectrum effective area image is identified, complementary areas are marked, and complementary effective marks are formed; then, carrying out weight assignment on the marks, and constructing a pixel weight table; and finally, fusion calculation and dynamic range compression are performed on the texture feature map and the temperature gradient map by using the weight table, so that a monitoring fusion imaging map is generated, and the technical problem of detail loss caused by sudden illumination change and weak temperature difference due to the lack of adaptive perception capability for scene content in the existing fusion method is solved.

The invention discloses a space-time image velocity measurement method and system based on multi-direction Gabor filtering, and the method comprises the steps: carrying out the filtering processing of a space-time image through Gabor filters in multiple directions, so as to enhance the texture features in multiple directions, carrying out the image quality evaluation of a Gabor filtering image in each direction, and obtaining the velocity measurement of the space-time image. The filtering direction with the best textural feature enhancement effect is screened out; a clustering point set with the highest comprehensive evaluation score is screened out through a two-layer evaluation screening mode of clustering quality evaluation and line segment quality evaluation, straight line fitting is carried out based on the clustering point set, an optimal flow velocity line can be obtained, and the river surface flow velocity is obtained through calculation; according to the method, a three-layer evaluation system composed of filtering direction evaluation, clustering quality evaluation and line segment quality evaluation is combined, a high-quality, correct and effective flow velocity line can be accurately extracted from a space-time image, and the accuracy and robustness of a flow velocity measurement result are greatly improved.

Embodiments of the disclosure provide a method, apparatus, electronic device and storage medium for processing image, and the method includes: obtaining an image to be processed; determining an object structural feature within the image to be processed corresponding to a target object and determining a style texture feature corresponding to a reference style image to be applied; and determining a target style image corresponding to the image to be processed based on the object structural feature and the style texture feature.

The invention discloses a plastic mold injection molding defect detection method and system, and the method comprises the following steps: collecting a surface image corresponding to a plastic mold injection molding part and an internal tomography image, carrying out the denoising, graying and image alignment, and meanwhile, generating a multi-dimensional image data set corresponding to each injection molding part through an associated index; extracting geometric features and texture features corresponding to each injection molding part based on the multi-dimensional image data set, and fusing the geometric features and the texture features to generate geometric texture vectors; performing similarity judgment estimation based on the geometric texture vector corresponding to each injection molding part and a preset defect-free injection molding part feature template, recursively determining a suspected defect area of each injection molding part, and estimating and generating an injection molding part defect type parameter table; and analyzing and generating injection molding part process defect optimization suggestions based on the injection molding part defect parameter table, and meanwhile, carrying out associated storage to generate an injection molding defect detection archive library. According to the invention, the mold cavity position corresponding to the defect can be positioned, and the mold injection molding defect detection efficiency can be greatly improved.

The invention belongs to the technical field of image processing, and discloses a cable tube well hole site occupation detection method based on multi-view image fusion, which comprises the following steps of: selecting a fixed position in a cable tube well, and acquiring images of the same well wall section from upper, lower, left, right and front five views by using handheld imaging equipment; performing guided filtering denoising and contrast-limited histogram equalization processing on the image to enhance the image quality; extracting a contour and calculating circularity, and determining a candidate circle center through a segmented recursive circle center positioning method; cross-view matching and verification are carried out on the holes with shielding, feature restoration is realized through centroid alignment and curvature similarity calculation, and the hole position state is judged by adopting a majority decision principle; and finally, performing joint decision-making based on the spatial positions, direction consistency and texture features of the cable and the hole, and generating and outputting an occupancy state matrix. The method effectively improves the recognition precision and robustness in a complex shielding environment, and is suitable for power resource management.

The invention discloses a GLCM texture feature extraction method and system based on dynamic multi-scale weighting, and relates to the technical field of image feature extraction, and the method comprises the steps: carrying out the image preprocessing and texture scale perception of an input image; carrying out the precise adaptation of the pixel distance and the texture scale of the processed image, dynamically adjusting the gray level of a gray level co-occurrence matrix GLCM according to the image texture complexity, and constructing a dynamic multi-scale GLCM; weight self-adaptive distribution is carried out on each texture scale and direction of the dynamic multi-scale GLCM, and original feature extraction is carried out in combination with distributed weights; and carrying out weighted fusion on the extracted original features to obtain fusion features, and carrying out anti-noise optimization to obtain standardized texture features. A scale parameter dynamic adjustment and feature weighting mechanism is adopted, the problems that a traditional GLCM is poor in adaptability to a complex texture scene and insufficient in feature discrimination degree are solved, and the characterization capacity of texture features to a measured sample and the feature discrimination degree and robustness in the complex texture scene are improved.

The invention relates to the technical field of image recognition and biological feature authentication, and particularly discloses a client identity real-time checking method based on image recognition. The method comprises the following steps: synchronously acquiring a certificate image, a real-time video stream and environmental parameters, and preprocessing to generate an environmental parameter matrix and a biological pulse sequence; certificate static texture features and video dynamic micro-expression features are separated and extracted through a double-flow network, and feature weights are dynamically adjusted and dimensionality reduction is carried out based on environmental parameters; the historical weight and the illumination factor are fused to generate a dynamic fusion coefficient, and feedback optimization feature extraction is carried out; decoding the iris tremor frequency to generate a living body confidence coefficient, combining the dynamic weight fusion features to obtain an initial similarity, and carrying out multiplication fusion on the initial similarity and the living body confidence coefficient; a micro-expression periodic jitter mode is detected, and risk factors are output after fraud punishment is applied; and triggering model re-calibration based on the risk factor. According to the method, the verification precision and the anti-counterfeiting capability under the scenes of complex illumination, head deflection and screen attack can be remarkably improved, and high-robustness identity authentication is realized.

The application relates to the technical field of image recognition, in particular to a bearing surface defect automatic detection system based on image recognition, which comprises an image acquisition and preprocessing module, a bearing surface image is acquired, the image is cropped and filtered to remove noise and adjust contrast, and a processed image is obtained; the processed image is subjected to gray scale conversion to generate a preprocessing result. In the application, environmental noise interference is reduced through image cropping and filtering, the contrast is adjusted to optimize image quality, and the distinguishing degree of a defect area and a background is improved. The gray scale conversion standardizes image channel information, keeps the calculation precision of feature extraction consistent, and avoids multi-channel data interference analysis results. An image pyramid method constructs different scale image levels, in the local contrast and texture feature extraction process, the adaptability to fine cracks and large-area peeling defects is improved, and the stable recognition ability to different size defects is enhanced.

The invention relates to the technical field of image processing, in particular to an image-based process pipeline valve opening degree detection method and system. The method comprises the steps that an initial image of the side face of a process pipeline valve to be detected is acquired, and texture feature values of pixel points of the initial image are determined; performing adaptive constraint on dark channel prior of the initial image by using the texture feature value, determining spatial adaptive transmissivity, and performing radiation brightness restoration on the initial image based on the spatial adaptive transmissivity; obtaining a restored image by using the spatial adaptive transmissivity and the radiance restoration result; and extracting a characteristic line of an indicating part of the valve in the restored image to determine the opening percentage of the valve. According to the technical scheme, the opening degree of the process pipeline valve can be detected more accurately.

The invention relates to the technical field of image data processing, in particular to a high-voltage switch shell surface coating uniformity evaluation method and system, and the method comprises the steps: collecting a shell surface coating image; dividing the image into a plurality of super-pixel areas by using a super-pixel segmentation algorithm introducing an adaptive distance measurement mechanism; extracting geometric structure features, texture features and adjacent color difference features of the target area; and carrying out nonlinear fusion on the adjacent chromatic aberration and geometric structure characteristics to construct inter-class characteristics, fusing the inter-class characteristics with texture characteristics serving as intra-class characteristics to obtain a comprehensive score, and judging whether the coating is uniform or not according to the comprehensive score. According to the method, the segmentation weight is adjusted in a self-adaptive manner, and the weak chromatic aberration is amplified in a nonlinear manner, so that accurate segmentation of a tiny defect region is realized; geometric irregularity, color mutation and texture anomaly features are integrated, detection of various tiny coating defects is achieved, and the evaluation accuracy is improved.

The invention relates to a garbage sorting identification method and device based on multi-feature fusion, and the method comprises the steps: carrying out the multi-dimensional feature extraction of an obtained garbage image, and obtaining a color feature, a texture feature and a shape feature; performing normalization processing on the color features, the texture features and the shape features; obtaining a color weight coefficient, a texture weight coefficient and a shape weight coefficient by adopting an attention mechanism based on the normalized color features, texture features and shape features; fusing the color features, the texture features and the shape features by adopting a feature pyramid to obtain fused features; carrying out dimension reduction enhancement processing on the fusion features; and garbage sorting identification is carried out based on the fusion features after dimension reduction enhancement processing. According to the method, the problem of feature judgment in a complex environment can be solved, the capacity of distinguishing garbage with similar appearances is remarkably improved, and the requirement of modern garbage sorting for high-precision recognition can be met.

The invention discloses a multi-modal endoscope image fusion analysis method, and relates to the technical field of medical image navigation, and the method comprises the steps: collecting an original white light image and an original narrow-band image, carrying out the preprocessing, and collecting real-time pose parameters; extracting blood vessel texture features in the preprocessed original white light image and the original narrow-band image, generating a binary semantic mask, calculating a spatial transformation matrix according to the binary semantic mask, and generating preliminary visual deformation field data; and calculating microscopic deformation field data according to a pre-stored biomechanical characteristic database, the preliminary visual deformation field data and the real-time pose parameters, carrying out reverse calculation to obtain physical deformation compensation field data, and carrying out spatial superposition operation on the physical deformation compensation field data and the preliminary visual deformation field data to obtain comprehensive compensation deformation field data. According to the method, real-time quantitative evaluation and abnormal early warning of the reliability of the fusion result are realized through multi-modal fusion quality early warning judgment, and the clinical decision credibility and the operation safety are enhanced.

The invention discloses a hierarchical image rain removal method based on enhanced rain stripe perception. According to the method, for the problem of image quality degradation in a rainy day environment, an enhanced rain stripe perception feature enhancement module E-RAFEM is designed, two core components of a learnable direction filter and rain stripe texture modeling are integrated, and the directivity and linear texture features of rain stripes are accurately modeled. An encoder-decoder backbone network based on Transform is adopted, and E-RAFEM modules are embedded in the first three encoding levels, so that a hierarchical rain stripe processing mechanism is formed. A progressive multi-scale fusion mechanism is introduced, multi-scale features are captured through convolution kernels with different expansion rates, and gradual integration is carried out in a progressive mode to avoid information loss. According to the enhanced rain stripe sensing network ERA-Net provided by the invention, high-quality recovery of images under various complex rainy day conditions is realized through accurate rain stripe feature modeling and hierarchical processing strategies.

The invention discloses a foreign matter layered detection method and system and electronic equipment, and is used for distinguishing foreign matter categories and levels based on feature engineering. The foreign matter layering detection method comprises the following steps: acquiring a first original image, a second original image, a third original image and a fourth original image of a display panel to be detected; performing homographic transformation on the third original image and the fourth original image to obtain a first transformed image and a second transformed image; respectively extracting shape features, texture features and gray features corresponding to the first original image, the second original image, the first transformation image and the second transformation image; performing standardization processing and normalization processing on the shape features, the texture features and the gray features; respectively carrying out evaluation and weight distribution on the processed shape features, the processed texture features and the processed gray features, and carrying out fusion to obtain a fusion result; and inputting the fusion result into a pre-constructed classifier, and outputting a classification result.