76 results about "Local binary patterns" patented technology

The invention relates to the technical field of remote sensing data processing and glacier area calculation, in particular to an unmanned aerial vehicle and satellite remote sensing data fused glacier area calculation method, which comprises the following steps of: cooperatively acquiring a satellite multispectral image and unmanned aerial vehicle high-resolution optical and LiDAR data, performing time synchronization, high-precision space registration and data enhancement processing, and calculating the glacier area through the unmanned aerial vehicle and satellite remote sensing data fusion. A satellite image glacier macroscopic feature and an initial mask are extracted by using a convolutional neural network and an NDSI / NDWI algorithm, and unmanned aerial vehicle image microscopic texture, edge and topographic features are acquired through a local binary pattern, edge detection and LiDAR point cloud; based on pyramid layering and a conditional random field, adopting a variance weighting algorithm to realize multi-scale feature level fusion; and after segmentation through an Otsu algorithm, calculating the area through a pixel counting method and introducing gradient correction, and evaluating the reliability through three types of precision. The method breaks through the limitation of a single data source, fuses macroscopic and microscopic features, improves the boundary positioning precision and calculation efficiency, and is suitable for glacier dynamic monitoring in a complex environment.

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 discloses a thyroid cancer pathological image classification method and system based on multi-modal feature fusion and Bayesian optimization, and relates to the technical field of medical image processing, and the method comprises the steps: collecting a thyroid ultrasound image data set, and carrying out the preprocessing operation; extracting an improved local binary pattern feature, a Haralick texture feature and a VGG16 depth feature, and constructing a mixed feature space; and splicing the features in the mixed feature space into a 4119-dimensional mixed feature vector, and carrying out feature importance screening by utilizing ExtraTres. Improved local binary pattern features, Haralick texture features and VGG16 depth features are fused, a mixed feature space is constructed, feature importance screening and PCA dimension reduction are performed by using ExtraTrees, multi-level features of the image are effectively extracted, the accuracy of benign and malignant thyroid nodule classification is remarkably improved, meanwhile, network hyper-parameters are dynamically adjusted through a Bayesian optimization algorithm, and the classification accuracy of benign and malignant thyroid nodules is improved. And model convergence is accelerated in combination with a cosine annealing strategy, so that the generalization ability of the model is enhanced, and the model can be excellently expressed on different data sets.

The invention provides a bearing fault diagnosis method based on a one-dimensional local binary pattern and a Hankel matrix. The bearing fault diagnosis method comprises the following steps: acquiring a discrete vibration signal; performing first-order differential operation on the discrete vibration signal to obtain a differential signal; performing inherent time scale decomposition on the differential signal to obtain an inherent rotation component signal; performing quantization and signal reconstruction on each inherent rotation component signal by taking a root mean square as a quantization criterion of a one-dimensional local binary mode method to obtain a decimal feature signal; constructing a Hankel matrix of the decimal characteristic signal and performing signal reconstruction according to a covariance matrix of the Hankel matrix; performing spectral analysis on the reconstructed signal, calculating the fault characteristic frequency of the bearing, and then judging the state and the fault type of the bearing through a frequency component obtained through spectral analysis and the fault characteristic frequency of the bearing obtained through calculation. According to the bearing fault diagnosis method, noise can be effectively suppressed, the bearing fault feature information can be effectively extracted, and the bearing state and the fault type can be accurately identified.

The invention relates to the technical field of crop disease and insect pest image recognition, and particularly discloses a crop disease and insect pest image recognition method based on a large model, and the method comprises the steps: obtaining a multi-angle leaf image through high-resolution imaging equipment under a controllable illumination condition, and obtaining a target image in a unified format; extracting scab texture complexity features in combination with a local binary pattern and a gray-level co-occurrence matrix algorithm, and performing multi-channel statistical analysis on RGB and HSV color spaces to generate color heterogeneity feature vectors; further fusing the two types of features into a composite disease feature vector, inputting the composite disease feature vector into a probability model constructed based on a support vector machine and a Monte Carlo Dropout mechanism, and outputting probability distribution and confidence score of disease and pest categories; and dynamically adjusting a model training strategy according to a confidence level, triggering a feedback mechanism for a low-confidence sample, generating a synthetic image by using a conditional generative adversarial network, and optimizing model parameters in combination with incremental learning to realize stable identification modeling of rare or complex disease types.

The invention discloses a discharge channel extraction method and system based on edge detection and texture feature fusion, and belongs to the technical field of power equipment fault diagnosis and digital image processing, and the method comprises the steps: obtaining an ultraviolet weak light image of power equipment, and carrying out the preprocessing; multi-scale edge detection is executed based on the preprocessed image, edge information is fused through a multi-scale voting mechanism, and a candidate edge graph is generated by combining direction consistency connection fracture edges; extracting texture features of the pre-processed image, wherein the texture features comprise a rotation invariant local binary pattern uniformity feature and a multi-direction gray level co-occurrence matrix feature; according to a dynamic distribution weight coefficient of a discharge type, carrying out weighted fusion on the edge intensity of the candidate edge graph and the texture features, and generating a discharge channel confidence score graph; and carrying out binarization segmentation and contour optimization processing on the confidence score graph of the discharge channel, and extracting morphology quantization parameters of the discharge channel.

The invention discloses an ultra-high-definition video stream adaptive coding method based on deep learning visual saliency, and the method comprises the steps: carrying out the five-scale Gaussian filtering processing and image pyramid construction of a video frame, and combining Sobel gradient, Laplacian edge and local binary pattern feature extraction to generate a multi-scale feature map; a pre-training saliency detection network is adopted, and a smooth saliency thermodynamic diagram is generated through processing of a feature adaptation layer, a residual encoder, a self-attention mechanism and a transposed convolution decoder; dividing the video frame into a high region, a middle region and a low region according to the saliency thermodynamic diagram, and establishing a regionalization coding parameter table; performing differentiated prediction modes, motion estimation and quantization strategies on different salient regions; and organizing coded data according to an H.265 / HEVC standard, and embedding the saliency thermodynamic diagram into supplementary enhancement information for transmission. According to the method, the important region concerned by the user can be intelligently identified, a differentiated coding strategy based on content semantics is realized, and the coding efficiency is remarkably improved.

The invention discloses a wood block surface flaw detection method based on image features, and belongs to the technical field of image data processing, and the method comprises the steps: obtaining an image of the surface of a target object, and carrying out the denoising and illumination normalization processing of the image, and obtaining a first image; performing wavelet transform decomposition to obtain edge distribution description data and shape description data corresponding to the first image, and generating geometric characteristic description; analyzing texture distribution of the first image in combination with a local binary pattern algorithm according to the geometric feature description, and generating a texture feature set; fusing the geometric feature description and the texture feature set to construct a multi-dimensional feature vector, and performing dimensionality reduction on the multi-dimensional feature vector by adopting principal component analysis to obtain a dimensionality-reduced feature vector; and inputting the dimension reduction feature vector into a support vector machine classifier, and outputting a defect classification result. The wood block surface flaw detection method based on the image features solves the problem that when an existing detection system faces complex flaw types, high-precision classification is difficult to achieve.

The invention relates to the technical field of machine vision detection, in particular to an automobile part surface defect online detection method and system. The method comprises the following steps: acquiring a grayscale image, calculating the size of a structural element for each pixel based on a local Gaussian Laplacian operator response variance, filtering to obtain a substrate image according to the size of the structural element, and differentiating to obtain a texture image. Determining a Gabor scale and a gray-level co-occurrence matrix statistical direction by using the size, and extracting a cooperative direction gray-level co-occurrence matrix feature; and a weight is set based on the size and is subjected to weighted fusion with a multi-scale rotation invariant local binary pattern feature to generate a texture saliency map, and texture defects are judged. On the substrate image, taking the gray value as the height, and determining a neighborhood calculation curvature feature based on the size to detect the substrate defect. According to the scheme, the image scale can be adaptively analyzed, the background texture is effectively inhibited, and therefore different types of tiny defects such as scratches and pits can be reliably detected.

The invention provides a mountainous area night animal tracking method and system based on multispectral and thermal imaging data fusion, and relates to the technical field of night animal tracking, and the method comprises the steps: synchronously obtaining a multispectral image sequence and a thermal imaging image sequence of a mountainous area night; performing multi-scale Retinex enhancement processing on the near-infrared image and the thermal image after registration to obtain an enhanced candidate target image; extracting a multi-source texture signature fusing a local binary pattern feature vector and a thermal gradient direction histogram for each candidate target, and constructing a multi-dimensional feature set in combination with geometric features obtained by morphological skeletonization; target association between adjacent frames is realized by adopting a nearest neighbor greedy matching algorithm, and a preliminary track segment is formed; and finally, removing abnormal trajectories, solving trajectory crossing and shielding conflicts through a sparse dynamic time warping algorithm, and generating an accurate and complete animal movement trajectory. According to the invention, conflicts caused by track crossing and shielding can be solved, and a continuous and accurate single animal movement track is output.

The invention discloses a sandstone slice image component identification method based on a GL-SLIC algorithm and semi-supervised learning. The method comprises the following steps: firstly, acquiring a sandstone slice image amplified by 200 times under single polarization, converting an RGB image into a CIE-Lab color space, and extracting a GLBP texture feature vector; then a GL-SLIC superpixel segmentation algorithm is constructed, and adaptive segmentation based on texture and color features is realized in combination with a Gabor filter and a local binary pattern; then implementing a region merging algorithm to generate complete mineral particles and pore fragments; finally, a classifier based on VGG16 and a discriminator based on ResNet18 are constructed, a semi-supervised self-training framework is adopted, a model is initialized by using about 6% of manual labeling samples, a high-confidence-coefficient pseudo-label extension training data set is generated through iteration, and automatic recognition of sandstone components such as quartz, pores, kaolinite, rock debris and a matrix is achieved. According to the method, the recognition accuracy of 96.3% on a test set is achieved, compared with a traditional method, the segmentation precision and the recognition accuracy are remarkably improved, the data labeling cost is greatly reduced, and an efficient technical scheme is provided for automatic analysis of geological images.

The invention provides an image classification method and device based on super-dimensional calculation, a medium and a product. The method comprises the following steps: extracting target texture features from an infrared image by using a local binary pattern; performing feature dimension reduction on the target texture features through a principal component analysis method to obtain one-dimensional structure feature information of the target; performing super-dimensional coding on the one-dimensional structure feature information of the target by using an efficient multivariate coding method to form a super-dimensional vector; constructing a super-dimensional vector data set by using the obtained super-dimensional vectors; constructing a target classification and recognition model based on a binary full-connection neural network; training a target classification recognition model by using the super-dimensional vector data set; and performing target recognition by using the trained target classification recognition model to obtain a target category. Target recognition is completed through combination of feature extraction, efficient super-dimensional coding and the binary full-connection neural network, the image classification recognition accuracy can be improved, and the method has the advantages of being low in super-dimensional coding complexity, high in model generalization ability and the like.

The invention discloses an AI linkage data system based on Internet of Things multi-source data fusion, and relates to the technical field of image recognition, and the system comprises a collection module which is used for collecting a real-time image of a fire, and selecting a suspected region with the brightness higher than a brightness threshold value in the real-time image; the preprocessing module is used for performing Gaussian filtering preprocessing on the suspected area; the method comprises the following steps: denoising a suspected region by dynamically adjusting Gaussian filtering parameters, calculating a brightness change rate of pixels on the inner and outer sides of an edge to obtain a gradient band width, dividing suspected region sub-blocks, extracting sub-block texture feature vectors by adopting a local binary pattern algorithm, and calculating a region overall texture entropy value by combining a region texture similarity matrix to obtain a region texture similarity matrix; the difference between the strong reflection light spots and real flames can be accurately recognized, the AI chip is prevented from misjudging the strong reflection light spots as newly added fire behaviors, then the linkage control module is prevented from mistakenly starting a spraying system in a non-fire behavior area, and damage of a large number of materials due to water stains caused by mistaken spraying is reduced.

The invention discloses a bridge concrete apparent damage identification method and system based on texture analysis, and belongs to the crossing field of bridge engineering detection and computer vision technology, and the method comprises the steps: employing an unmanned plane to collect a bridge member surface image, and generating an orthoimage; extracting contrast, correlation and entropy features based on the gray-level co-occurrence matrix; extracting histogram features based on a local binary pattern; detecting a corrosion dialysis area based on an HSV color space; fusing the multi-dimensional features, inputting the fused multi-dimensional features into a multi-label classifier, and outputting pixel-level segmentation masks of various damages such as cracks, spalling, exposed reinforcement corrosion and dialysis at the same time; according to the method, the complementary texture features are fused to realize simultaneous identification of multiple types of damages, a pixel-level segmentation result is output, and an evaluation report conforming to engineering specifications is automatically generated.

The invention provides a video coding method, which comprises the following steps of: inputting a coding tree unit to be coded, and extracting texture features of a coding block, the texture features comprising local binary pattern similarity of all pixel points in the coding block and direction dispersion of each pixel point; inputting the texture features into the trained multi-type division prediction architecture, and sequentially judging whether to execute quadtree division, horizontal or vertical division, horizontal binary tree or ternary tree division and vertical binary tree or ternary tree division or not through a binary classification problem method; and calculating the rate distortion cost under each division mode to obtain an optimal division mode and a suboptimal division mode of the coding block, and outputting an optimal mode or a combination of the optimal mode and the suboptimal mode. According to the invention, the accuracy of the prediction mode in coding is improved.

The present invention relates to a wire rope surface defect recognition method based on feature fusion, which is in the field of wire rope surface defect recognition technology. The method comprises the following steps: graying a wire rope defect image, dividing the image into blocks, improving a traditional LBP algorithm using a central multi-scale local binary pattern based on the image blocks, extracting texture feature information of the divided image, performing PCA dimensionality reduction based on the obtained image texture features, and finally extracting global image texture features using GLCM and performing feature fusion with the reduced image texture features; and performing wire rope surface defect recognition and classification using an SVM classifier. The method solves the problem that traditional local binary patterns (LBP) are easily affected by central pixels and noise and cannot accurately identify wire rope surface defects. The method achieves an overall recognition rate of 97.5% for wire rope surface defects, which is at least 5% higher than other algorithms. The method can effectively identify various defects on the wire rope surface.

The present application provides an image processing method, device and equipment, which can be applied to the technical field of image processing. The image processing method comprises: pre-processing an input image to obtain input features; inputting the input features into a visual encoder and a multi-layer perception machine in a visual center decoupler respectively to obtain enhanced special features and salient object detection special features; the visual encoder aggregates local region features based on the input features to obtain the enhanced special features, and the multi-layer perception machine captures edge information based on the input features to obtain the salient object detection special features; inputting the enhanced special features into an enhancement network to obtain enhanced output features; the enhancement network takes illumination weights of different color channels and local binary pattern features of the input image as illumination constraints, and enhances the enhanced special features to obtain the enhanced output features; and inputting the salient object detection special features and the enhanced output features into a salient object detection network to detect a salient object.

The invention relates to a material processing method and system based on sensitive content detection and a medium, and belongs to the technical field of multimedia content security. The material processing method comprises the following steps: receiving an input original material, and analyzing the input original material into an image stream, a video stream and a text stream; executing HSV color space conversion, and extracting texture features; performing semantic analysis on the text stream to generate a word segmentation sequence and a named entity recognition result; random frequency noise is injected based on the saturation channel, and local binary pattern features are extracted from the saturation channel after noise injection; generating a semantic vector according to the word segmentation sequence and a corresponding named entity recognition result, and calculating a risk entropy value; and establishing a space-time mapping relationship between the image stream / video stream and the text stream, constructing a cross-modal incidence matrix, executing a grading decision according to an output result, executing a material processing operation, and feeding back a processing result to the cross-modal incidence matrix for weight updating. The sensitive content identification accuracy and timeliness can be improved.

This invention discloses a method and system for identifying apparent damage to bridge concrete based on texture analysis, belonging to the interdisciplinary field of bridge engineering inspection and computer vision technology. The method includes: acquiring surface images of bridge components using a drone and generating orthophotos; extracting contrast, correlation, and entropy features based on the gray-level co-occurrence matrix; extracting histogram features based on local binary patterns; detecting corrosion and seepage areas based on the HSV color space; fusing multi-dimensional features and inputting them into a multi-label classifier, simultaneously outputting pixel-level segmentation masks for multiple types of damage such as cracks, spalling, exposed rebar corrosion, and seepage; generating a damage level score and maintenance recommendation report according to bridge technical condition assessment standards. This invention integrates complementary texture features to achieve simultaneous identification of multiple types of damage, outputs pixel-level segmentation results, and automatically generates an assessment report that conforms to engineering specifications.

The invention relates to the technical field of artistic pattern recognition, and provides an artistic pattern recognition system based on artificial intelligence, which comprises a data acquisition module, a feature extraction module, a feature selection module, a texture analysis module and a result evaluation module.The artistic pattern recognition system overcomes the defects in the prior art and is reasonable in design and high in practicability. A digital image of the surface of an artwork is obtained through high-resolution digital imaging equipment, denoising, white balance correction and image enhancement processing are carried out to ensure image quality, then texture feature vectors are extracted through a rotation invariant local binary pattern method, texture features can be effectively extracted, rotation invariance is achieved, and classification robustness is improved; the screened features are input into the pre-trained deep learning model, the artistic pattern category probability is output, the deep learning model is used for learning texture features, and the generalization ability and classification performance of the model are improved.

This invention belongs to the field of surface defect detection technology for spring products, and relates to a machine vision-based method for detecting surface defects in springs. The method includes the following steps: acquiring a sequence of surface images of the spring under multiple preset rotation angles; for the images to be registered in the image sequence, extracting the spiral contour of the spring through edge detection and curve fitting, calculating the curvature along the spiral contour, and identifying local extreme points where the absolute value of the curvature exceeds a preset curvature threshold as key points; for each key point, calculating local binary pattern features and gradient direction histogram features in the neighborhood of the key point, and weighting and fusing the two features according to the local curvature value at the key point to generate a hybrid feature representation, which can improve the reliability of spring surface defect detection and reduce the false negative and false positive rates.

The invention provides a tooth image caries recognition method based on frequency domain enhancement, and relates to the technical field of image processing, the method adopts a dynamically adjusted annular band-pass filter to adaptively enhance an intermediate frequency component of a tooth image caries region in a frequency domain, so that the feature discrimination can be effectively improved, and the recognition accuracy is improved. Based on improved phase consistency edge optimization and gamma correction, the quality of a tooth image can be remarkably improved, multi-modal features are extracted through an improved local binary pattern and a gray-level co-occurrence matrix, and a dual-channel deep convolutional network is constructed to realize fusion of global and local features, so that the image quality is improved. The caries probability heat map of the tooth image can be accurately generated, and accurate positioning of the caries area can be realized by adopting adaptive threshold segmentation and morphological processing.

The application provides a mountainous area night animal tracking method and system based on multispectral and thermal imaging data fusion, and relates to the technical field of night animal tracking, and comprises the following steps: synchronously acquiring a multispectral image sequence and a thermal imaging image sequence in a mountainous area at night; performing multiscale Retinex enhancement processing on a registered near-infrared image and a thermal image respectively to obtain a strengthened candidate target image; extracting a multi-source texture signature of a fusion local binary pattern feature vector and a thermal gradient direction histogram for each candidate target, and combining geometric features obtained through morphological skeletonization to construct a multi-dimensional feature set; realizing target correlation between adjacent frames by using a nearest neighbor greedy matching algorithm to form a preliminary trajectory segment; and finally eliminating abnormal trajectories, solving trajectory intersection and occlusion conflicts through a sparse dynamic time warping algorithm, and generating an accurate and complete animal moving trajectory. The application can solve the conflicts caused by trajectory intersection and occlusion, and output continuous and accurate single animal moving trajectories.

This invention discloses a coal and rock image recognition and classification method based on the fusion of improved CLBP and receptive field theory, belonging to the field of image processing technology. First, the coal and rock image is converted to grayscale, and the pixel values ​​of each point in the grayscale image are extracted to form a pixel value matrix. Then, median processing is used to denoise the pixel matrix to enhance the algorithm's noise resistance. Based on the new pixel matrix obtained after median processing, an improved Complete Local Binary Pattern (CLBP) image feature descriptor is introduced for coal and rock feature extraction. Finally, a network model is used for recognition and classification. Experimental results show that the recognition accuracy of coal and rock in the coal and rock database reaches 94%, and the recognition speed is also better than the original algorithm. This invention can effectively solve the problem of low recognition efficiency of the original algorithm and can efficiently solve practical problems in coal and rock recognition methods, possessing certain practical value.

The invention provides a motion texture joint threshold (WTJT) method, which comprises the following steps of: firstly, comparing the motion speed and the motion vector of a current frame with the motion speed and the motion vector of a previous frame and a next frame through the sum of absolute differences (SAD) in a frame interpolation information acquisition mode, and screening candidate jump blocks; secondly, extracting two textural features of direction and frequency details from adjacent frames based on a local binary pattern (LBP), and screening candidate hop blocks again in combination with Gabor filtering; and finally, performing spatial-temporal feature decomposition on an input video sequence, establishing a proportional relation between motion information and texture feature information, and calculating a joint weighting coefficient (JWC) so as to dynamically adjust a block jumping threshold value of each frame and realize adaptive allocation of a block jumping proportion. Through united weighting after motion and texture features are normalized, the method provided by the invention improves the accuracy of block jumping judgment, optimizes the distribution of block jumping rate, and achieves balance between algorithm complexity and reconstruction quality.

The present invention relates to a method for identifying and inverting internal solitary waves in pattern data based on machine learning, and belongs to the field of ocean observation technology. The present invention combines Mask-RCNN and variational autoencoders, first preprocesses and images the sea surface height data, extracts texture features through a local binary pattern algorithm, constructs a labeled data set and performs model training, and identifies internal solitary wave stripes and their positions therefrom; then, through the extraction and processing of temperature field data, uses a variational autoencoder to invert the temperature field at depth; and extracts the amplitude of the internal solitary wave in the temperature field based on the internal wave identification result. The present invention can efficiently and accurately process large-scale, refined ocean pattern data, significantly improving the efficiency and accuracy of data processing. The present invention has broad application prospects, especially in marine disaster warning, marine scientific research, and internal wave dynamics research, and can greatly improve the automatic identification and amplitude extraction capabilities of internal waves.

This invention discloses an intelligent fire detection method based on artificial intelligence video analysis, belonging to the field of artificial intelligence video analysis technology. This method targets multiple video streams deployed on an edge computing gateway. It utilizes an asynchronous decoupled buffer queue to bind video frames and timestamps into frame units to be processed. An incremental sequence identifier is assigned to each frame unit to be processed via a hardware resource scheduler, and the frame units are then distributed to a first processing pipeline and a second processing pipeline based on the sequence identifier. In the first processing pipeline, background removal and dynamic texture extraction are performed to generate a thermal activation map. In the second processing pipeline, frequency domain transformation and multi-scale local binary mode decomposition are performed to generate a flame frequency domain texture feature map. The thermal activation map and the flame frequency domain texture feature map are then pixel-level weighted fusion at the same time reference to generate a fusion confidence tensor. Finally, non-maximum suppression and spatial connectivity analysis are performed to output the bounding box of the flame target and the initial fire intensity level.

The application discloses a kind of offshore area high-resolution three-dimensional reconstruction method and system, including using perspective size invariant feature to match the multi-view angle image of offshore area, according to the feature similarity guide sampling, obtain sparse feature point cloud;Based on structured local binary pattern, the depth of each pixel is obtained using graph cut method, and the sparse feature point cloud is encrypted to generate dense point cloud;Global error optimization is carried out to the point cloud of all view angles, and according to the depth information of the matching feature points, the three-dimensional coordinates of the feature points in space are generated;Strong and weak connection relationship between plane primitives is constructed, and the surface model is obtained by graph structure and energy function.The application can obtain wide area large range panoramic high-resolution information of sea area, construct three-dimensional model, and can intuitively display the space-time big data of offshore area.

The invention relates to the technical field of image analysis, and discloses a PCB (Printed Circuit Board) production defect detection method based on image analysis, which comprises the following steps: acquiring a surface image of a PCB, and carrying out image preprocessing on the surface image, including graying, noise filtering and image enhancement; multi-scale feature extraction is carried out on the preprocessed image to obtain feature maps under multiple scales, and the multi-scale feature extraction is based on Gaussian pyramid decomposition and local binary pattern texture feature fusion; and based on the feature map, performing registration comparison on the collected feature map and a feature map of a standard template. According to the method, through fusion of multi-scale image feature extraction and defect quantitative analysis, the recognition precision of PCB tiny defects and dense line anomalies is remarkably improved, the limitation that a traditional detection means is insufficient in subtle feature perception capacity is effectively overcome, and accurate mapping from the image level to defect features is achieved.