402 results about "Image alignment" patented technology

The invention relates to the technical field of building structure health monitoring, in particular to a building defect detection method based on infrared thermal imaging and visible light bimodal fusion and a matched intelligent imaging device. According to the method, infrared thermal imaging and visible light bimodal image fusion are combined, and an improved YOLOv8m-seg model is combined, so that accurate detection of the defects of the outer wall of the building is realized. Firstly, a bimodal data set containing multi-material temperature difference data is constructed, ORB feature matching is combined with a feature alignment module (FAM) to achieve accurate image alignment, the defect detection robustness is improved through an improved bimodal feature fusion network, and finally automatic recognition and quantitative analysis of cracks, fractures and other defects are achieved. The problems that single-mode detection is interfered by dirt and is poor in multi-material adaptability are solved, the detection accuracy rate reaches 96% or above, the recall rate exceeds 93%, the single image detection time is shorter than or equal to 0.3 s, and the method is suitable for efficient detection of various building outer wall defects.

The invention relates to the technical field of image processing, and discloses an infrared image enhancement method and system based on local phase correlation, and the method comprises the steps: obtaining a plurality of continuous frames of infrared images, carrying out the intelligent partitioning of a reference frame, and calculating the variance feature, the method comprises the following steps: selecting regions of interest with rich information, independently executing phase correlation operation in each region to extract a local translation vector, obtaining global displacement estimation through weighted fusion, adopting an abnormal value detection algorithm to improve robustness, and finally realizing sub-pixel-level image alignment and intelligent weighted fusion. The method is suitable for real-time enhancement processing of satellite-borne infrared remote sensing images, the resource constraint requirement of an embedded platform is met while the processing quality is guaranteed, and an efficient and reliable technical scheme is provided for space remote sensing image processing.

The invention provides a biodiversity monitoring method and device and terminal equipment, and is suitable for the technical field of data processing, and the method comprises the steps: obtaining biological image information and biological sound information; performing feature extraction on the biological sound information to obtain multiple pieces of biological audio feature information; performing feature extraction on the biological image information to obtain multiple pieces of biological image feature information; performing alignment processing on the biological audio feature information and the biological image feature information to obtain a biological audio alignment feature and a biological image alignment feature; enhancing the biological audio alignment feature and the biological image alignment feature to obtain a biological audio monitoring feature and a biological image monitoring feature; and monitoring organisms according to the biological audio monitoring features and the biological image monitoring features. According to the invention, the audio information and the image information are combined, so that the accuracy and comprehensiveness of real-time monitoring of biodiversity are improved.

The invention discloses a task-oriented grabbing method and system based on cross-level constraint reasoning, and belongs to the technical field of robot grabbing control. The method comprises the following steps: aligning a text instruction with an input image through a mask alignment module, generating a target area mask by utilizing SAM-Clip, and generating a target point cloud by taking the target area mask as spatial prior; and further analyzing an internal physical structure of the target by using VLM, guiding to generate a 6-DOF grabbing attitude, performing collision detection and quality scoring by combining high-level function and bottom-level geometric prior, and outputting an optimal grabbing attitude. According to the method, the problems that task understanding and scene perception are disjointed, and the grabbing posture lacks constraint are solved, and the grabbing success rate and the intelligent level of the robot in the open environment are remarkably improved.

The invention discloses a tumor space-occupying brain network neural image alignment method based on multi-modal fusion, and belongs to the technical field of medical image processing and artificial intelligence crossing. The method comprises the following core steps of multi-modal image heterogeneous feature decoupling, tumor occupation deformation field modeling, functional network topological structure maintenance, cross-modal feature adversarial alignment, dynamic deformation constraint optimization and clinical interpretability verification, and construction of a three-dimensional non-rigid registration network based on a double attention mechanism. And differential homeomorphic mapping of a tumor focus area and normal brain tissue is realized through the cascaded spatial transformation module. Aiming at the problems of insufficient multi-modal feature alignment and brain network topology distortion in the prior art, the invention provides a function connection constrained cross-modal fusion strategy, a graph convolution network is adopted to encode resting state function connection features, and network node displacement caused by tumor occupation is dynamically corrected in combination with deformable convolution and a bidirectional feature competition mechanism; a space consistency loss function based on white matter fiber bundle tracing is designed, and through diffusion tensor imaging feature guide structure-function bimodal joint optimization, the problems of insufficient registration precision in a focus area and whole brain network connection distortion of a traditional method are solved. Experiments show that the registration precision of the method in glioma cases reaches 0.82 mm and is improved by 37% compared with that of a traditional method, and dissection-function consistency of functional network reconstruction around tumors is remarkably improved.

The invention discloses a house full life cycle intelligent monitoring method based on machine vision, particularly relates to the field of machine vision, and is used for solving the problems of insufficient image alignment precision, aging change recognition lag and incapability of accurately quantifying life cycle evolution trend in existing house structure aging detection. The method comprises the following steps of: acquiring an image sequence by setting a shooting point position of a house facade component, performing spatial composite alignment by utilizing affine transformation and feature edge fitting, extracting component key points in an aligned image, dividing the component key points into segmented regions, and constructing a structure life cycle image stacking group; then calculating color distribution dispersion and texture roughness entropy of each section of region, establishing an aging evolution feature sequence, executing trend mutation point detection, extracting a material state jump section, and constructing an aging trend jump map; and finally, based on the map, identifying a jump risk area and outputting house component aging information, thereby realizing accurate monitoring and trend study and judgment of the house outer wall structure state.

The invention discloses a multi-frame photoacoustic image reconstruction method based on optical flow alignment and depth feature fusion. The method comprises the following steps: S1, obtaining photoacoustic signal data; s2, reconstructing a photoacoustic cross-sectional image; s3, performing optical flow calculation and image alignment; and S4, training the deep feature fusion network. According to the method, through optical flow motion correction and a potential space learning mechanism, space-time information and complementary features in the aligned multiple frames of images are dynamically integrated, complementary information in the multiple frames of images is adaptively fused, noise is suppressed, and finally reconstruction of high signal-to-noise ratio and high spatial resolution images of biological tissues is achieved. Experimental results show that the method can significantly improve image quality, recover image distortion and detail loss caused by motion and noise, and provide a new effective scheme for promoting robust clinical application of a photoacoustic imaging technology.

The invention provides a training-free text-image generation method based on a diffusion model, and relates to the technical field of computer graphic processing and artificial intelligence. The method comprises the following steps: extracting semantic phrases and layout information in an input text by utilizing a natural language model, inputting the input text, the semantic phrases and the layout information as additional conditions into a diffusion model, and extracting cross attention maps of different time steps; a positive and negative sample concept and a foreground and background concept based on an object are constructed, a new loss function is calculated on a cross attention map for semantic information and layout information, the loss function combines semantic loss, regional loss and original loss of a diffusion model and is used for updating a potential space image, and the image is generated through iterative denoising and a decoder. According to the method, additional training is not needed, image generation output based on the diffusion model better meets text requirements, and a better text and image alignment effect is achieved.

The invention discloses a brain multi-modal multi-sequence data registration method and device based on deep learning. The method comprises the steps of performing first iteration processing on a target image and a first moving image to obtain a first deformation field, wherein the first iteration processing comprises image alignment constraint processing of the target image, smooth constraint processing of the first deformation field, and area alignment constraint processing of a tumor area in the image; registering the first moving image to the target image based on the first deformation field to obtain a second moving image; performing second iteration processing on the target image and the second moving image to obtain a second deformation field; and registering the second moving image to the target image based on the second deformation field to obtain a registered moving image. In the process of performing unsupervised training on the deep learning model, after multiple times of iterative processing, model parameters are updated based on image alignment constraint loss with a target image, deformation field smooth constraint loss and region alignment constraint loss of a tumor region in the image.

The invention belongs to the field of characterization learning, and particularly relates to a prostate cancer diagnosis method based on a multimodal large model prompt learning mechanism. Comprising the following steps: step 1, data preprocessing; 2, key frames and similarity are calculated, and irrelevant editing is compressed; step 3, text and image alignment training; step 4, a test stage; according to the method, a similarity-based screening mechanism is provided, ultrasonic videos under coarse-grained labels are preliminarily screened under segmentation of a large model, and focus areas are focused on in time sequence; meanwhile, a pre-processing mechanism based on a large model is provided, and the influence of a large amount of irrelevant information existing in prostate ultrasonic image scanning on the model is compressed at a data end, so that the diagnosis effect of the model is improved.

The invention discloses an HDR video reconstruction method based on a standardized stream, and belongs to the technical field of high dynamic range image processing. The method comprises the following steps of: firstly, constructing a convolution optical flow estimation module with a self-adaptive normalized structure, wherein the convolution optical flow estimation module is used for accurately acquiring optical flow information between adjacent frames in an alternative exposure LDR video image sequence; then, carrying out multi-level feature alignment on the image sequence through an image alignment module so as to reduce alignment errors caused by illumination difference and movement; and finally, inputting the aligned and fused multi-level LDR image features into a standardized flow reconstruction network to realize high-quality HDR video image reconstruction. Aiming at the video reconstruction problem under the alternate exposure condition, the invention designs a standardized flow modeling structure considering the optical flow estimation precision and the feature alignment effect, and effectively improves the HDR video reconstruction quality in a complex dynamic scene.

The invention relates to the technical field of image processing, and discloses a fluorescent lifetime microscopic image large-view-field splicing method and system and electronic equipment, and the method comprises the steps: constructing a tissue region mask for a to-be-spliced image, and constructing a vignetting model in a tissue region to complete vignetting correction; counting brightness indexes in an organization area to determine a global brightness reference, and realizing image group brightness unification through global zooming and single image brightness adaptive correction; further realizing alignment of adjacent images through feature point matching, and finishing image block splicing in combination with a minimum color difference suture fusion method to obtain an image band; an overlapping area is extracted from an image belt to generate an effective content mask, relative displacement is estimated by using a phase correlation method after low-frequency suppression, image belt splicing is completed by multiplexing a minimum color difference suture fusion method, a large-view-field spliced image is output, the automation degree and robustness of splicing are improved, the spliced image is geometrically consistent and visually seamless, and the splicing efficiency is improved. And the requirements of medical research on high-resolution and large-field-of-view fluorescence lifetime microscopic images are met.

The invention discloses a multi-modal fine-grained semantic alignment method and device based on a graph neural network, and relates to the field of multi-modal deep learning. Firstly, deep feature extraction is performed on input multi-modal original data, and then word-level text features and local image features are constructed into a cross-modal graph structure. And performing weighted aggregation on node neighborhood information of the cross-modal graph structure through the graph attention network. And finally, carrying out weighted fusion on the text alignment features and the image alignment features. According to the cross-modal feature fusion method, the word-level text features and the local image features are uniformly abstracted into the graph structure nodes for refined alignment, a more accurate cross-modal semantic corresponding relation can be captured, the heterogeneity problem in expression modes and semantic structures is effectively relieved, and the accuracy and reliability of cross-modal feature fusion are improved. The graph attention network can adaptively adjust the weight distribution of information propagation, highlights the effect of key features in the alignment process, and ensures that the model makes full use of important semantic relationships.

The invention provides a panoramic image intelligent splicing method based on context semantics. The method comprises the following steps: firstly, obtaining and preprocessing a sequence image; extracting a depth feature map of the image by using a pre-trained depth network; performing semantic-guided feature matching and image alignment based on the feature map; an optimal suture line is generated according to the semantic region boundary, and fusion is carried out by adopting a semantic weighted multi-band fusion algorithm; and finally, detecting and repairing the semantic inconsistent region to ensure the semantic coherence of the panoramic image. According to the method, matching is carried out by using the high-dimensional feature tensor rich in semantic information extracted by the deep neural network, the problem of feature matching ambiguity caused by repeated textures and weak texture regions such as sky or white walls is solved, the semantic information is used as a strong constraint, regions with similar appearances but different semantics can be effectively distinguished, and the accuracy of feature matching is improved. Therefore, correct matching point pairs with consistent semantics are screened out from massive candidates, and the matching accuracy is greatly improved.

The embodiment of the invention provides a tunnel disease detection method and system based on an unmanned aerial vehicle, and belongs to the technical field of defect optical detection. The method comprises the steps that an unmanned aerial vehicle is controlled to fly along a tunnel to collect multichannel image data of the surface of a structure, and the flight attitude is adjusted based on environment illumination information to execute image illumination compensation; performing image alignment of each target anchor point based on a structure anchor point atlas constructed based on historical acquisition images in combination with the flight pose information of the unmanned aerial vehicle and the multi-channel image data after illumination compensation; identifying a disease area in the aligned image, extracting disease features of each target anchor point at this time, and updating the disease of each target anchor point at this time into a time sequence feature data sequence corresponding to each target anchor point; and based on the updated time sequence characteristic data sequence of each target anchor point, Bayesian point change detection is adopted to analyze the disease evolution trend of the tunnel. According to the scheme, the alignment precision, the time sequence comparability and the risk judgment capability of tunnel disease detection are integrally improved.

The invention discloses an SPR (Surface Plasmon Resonance) image optimization processing method based on image segmentation and edge enhancement, which comprises the following steps: acquiring SPR image data, and preprocessing to generate standardized SPR image data; inputting a structure boundary extraction model constructed based on CGNet, generating a structure boundary label graph, and aligning the structure boundary label graph with the image; gradient amplitude and local entropy mutation detection is executed, and an artifact guide graph is generated; respectively inputting the standardized image into details and context branches of the improved CSDNet, and extracting edge and semantic feature maps; inputting a guide perception gating module, executing structure enhancement and artifact suppression fusion, and generating a fusion feature map; inputting into a multi-scale detail recovery module, and outputting an edge enhanced image; and executing structural similarity and marginal definition scoring based on the original image and the enhanced image, and generating an optimization result. According to the method, synchronous optimization of SPR image edge enhancement and structure maintenance is realized, and the image definition and diagnosis availability are remarkably improved.

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 discloses a hierarchical three-dimensional scene generation method and system based on spatial super-division, and belongs to the technical field of computer graphics, and the method comprises the steps: carrying out the preprocessing of a scene image, and obtaining a high-resolution object image; generating initial rough scene voxels for the scene image, and screening rough voxels and structural latent variables aligned with the high-resolution object image from the initial rough scene voxels to construct a hierarchical scene tree; inputting the high-resolution object image and the rough voxel into a voxel super-resolution model, and generating a fine voxel which keeps geometric consistency with the rough voxel; performing scale alignment and attitude registration based on the rough voxels and the fine voxels; and generating fine voxels of the sub-components recursively by taking the rough voxels of the current node as conditions based on the hierarchical scene tree, and finally assembling to generate a high-resolution three-dimensional scene. According to the method, a high-quality three-dimensional scene with high visual fidelity, fine geometric details and global structure consistency can be efficiently and automatically reconstructed from a single RGB image.

The embodiments of this application provide an image data collection system, an image model training method, and a device for improving image resolution. In this application, an image capturing device is used to capture images of an object at different focal lengths to obtain a first image and a second image respectively, and the first image and the second image are processed to obtain a first processed image with high resolution and a second processed image with low resolution, respectively. Image alignment is performed on these processed images to obtain a high-resolution and low-resolution image pair. Many high-resolution and low-resolution image pairs are collected as a training image dataset to train a model for upgrading low-resolution images to high-resolution images. The trained model can significantly improve the ability to restore image details.

The invention provides a camera pose optimization method and a point cloud and image alignment method, and relates to the technical field of computer vision and three-dimensional reconstruction. The camera pose optimization method comprises the steps of obtaining point cloud data collected by a laser radar in a target scene and a pose sequence of the laser radar when the point cloud data are collected; based on the pose sequence and external parameter data between the laser radar and the camera, camera poses corresponding to multiple frames of target images collected by the camera in the target scene are determined, and space coordinates of the same target feature points in the multiple frames of target images are determined; performing joint optimization on the camera poses and the space coordinates of the target feature points corresponding to the multiple frames of target images; and on the basis of the camera poses corresponding to the optimized multiple frames of target images, incrementally registering new images collected by the camera in the target scene, and iteratively executing the steps of determining the space coordinates of the target feature points and optimizing the camera poses for a registered image set including the new images.

The invention relates to the technical field of image processing, in particular to an attention-based registration method and device for medical image registration, and the method comprises the steps: obtaining a fixed image and a moving image, and carrying out the preprocessing of the fixed image and the moving image, and splicing the images into a dual-channel input; extracting and fusing multi-scale features through a multi-scale feature stacking block in the lightweight feature extraction network; performing channel and space double attention enhancement on the fusion features; based on the enhanced features, a three-dimensional deformation field is generated through a decoder under unsupervised loss constraints; and transforming the moving image by using the deformation field to realize alignment with the fixed image. According to the method, a lightweight multi-scale feature stacking structure and a double attention mechanism are introduced, so that the high-precision image alignment capability is maintained, the model parameter quantity and the calculation complexity are remarkably reduced, the applicability and the reasoning efficiency on resource-constrained equipment are improved, and rapid, stable and high-quality medical image registration is realized.

The invention discloses a target detection method based on adaptive fusion of visible light and infrared features, and the method comprises the steps: firstly, achieving the image alignment and brightness standardization through synchronous collection of visible light and infrared images, and employing geometric registration and normalization processing; then extracting multi-scale features by adopting a multi-scale convolutional network, and performing deep fusion through a cross-modal attention mechanism and an adaptive modal weight to generate fusion features with higher discrimination; a channel attention module is further combined to perform channel-by-channel enhancement on fusion features, weak and small target features are effectively highlighted, and background interference is suppressed; and finally, candidate frame regression, detection result optimization and trajectory smoothing are realized through anchor frame detection, non-maximum suppression and Kalman filtering. According to the method, multi-dimensional optimization of the weak and small target from a feature layer to a decision-making layer is realized, and the method is suitable for various application scenes such as coast defense monitoring, security early warning and unmanned system navigation.

The invention relates to the technical field of robot intelligent perception, in particular to a multi-source perception and semantic mapping method for a field geological survey robot. According to the method, data of a laser radar, an inertial measurement unit and a visual sensor are fused based on an error state iteration Kalman filtering framework, the image alignment precision is improved by using plane information provided by point cloud data of the laser radar, and then a high-quality RGB image of the visual sensor is selected to dynamically update reference textures. And tight coupling updating is directly performed on the original point cloud and the pixel data, and a map is constructed. And on the basis, semantic recognition is performed on the point cloud by using a deep learning model, so that a point cloud map with rich semantic information is generated, and the environment understanding and task execution capabilities of the quadruped robot in a field environment are improved.

The invention discloses a rigid registration method and system for visible light and infrared images of an unmanned aerial vehicle, and the method comprises the steps: gradually achieving the high-precision cross-modal image alignment through rough registration and fine registration based on a bimodal feature interaction and causal attention enhancement mechanism; therefore, a stable spatial transformation result can still be obtained under the complex conditions of low signal-to-noise ratio, local shielding and the like. According to the method, high-precision rigid registration of the infrared and visible light images of the unmanned aerial vehicle under the condition of significant modal difference and scale difference can be realized, a registration task is modeled as a long sequence dependence problem by referring to a vision mechanism of step-by-step partition comparison when people observe a large scene, and a CALS module is introduced to gradually focus a local area, so that the registration accuracy is improved. Confusion caused by information overload in traditional global modeling is avoided, and therefore balance is achieved between global scale consistency and local geometric accuracy.

The invention discloses a structured corpus generation method and device for geological map multi-modal large model training, and the method comprises the steps: preprocessing geological vector data, and constructing a semantic aligned geological data set; randomly selecting a data viewport, and setting the geographic space range and size of a map viewport; configuring a plurality of visual parameter items, performing controllable random adjustment on text labels, element symbols and map maps, and customizing and producing geological map samples as required; extracting multi-dimensional attribute information of the geological map sample to construct structured meta-information annotation data, and constructing a geological element mask annotation file to obtain a structured corpus for geological map multi-modal large model training. According to the method, large-scale production of the training samples with diversity, high quality and image-text alignment features is realized, the geological map corpus meets the requirements of multi-modal large model training on data scale, diversity, labeling precision and modal alignment, and the training efficiency and generalization ability of a geological map intelligent interpretation understanding model are remarkably improved.

The invention relates to the field of medical image processing, in particular to a cross-modal medical image alignment method based on anatomical feature one-dimensional distribution, and the method comprises the steps: obtaining a computed tomography (CT) image sequence and a magnetic resonance imaging (MRI) image sequence of a hip joint region of a patient; extracting a CT one-dimensional anatomical feature sequence representing the morphological change of the first side femoral head from the CT image sequence; extracting an MRI one-dimensional anatomical feature sequence representing the morphological change of the first side femoral head from the MRI image sequence; based on the CT one-dimensional anatomical feature sequence and the MRI one-dimensional anatomical feature sequence, a similarity alignment process is executed; wherein the similarity alignment process comprises the following steps: determining an optimal similarity alignment offset between a CT image sequence and an MRI image sequence, and aligning the MRI image sequence and the CT image sequence according to the optimal similarity alignment offset. According to the method, the cross-modal image alignment problem is subjected to dimensionality reduction into one-dimensional anatomical feature sequence matching, so that the precision, efficiency and robustness of registration are remarkably improved.

The invention discloses an image super-resolution reconstruction method and device fusing image restoration and fast diffusion, and the method comprises the steps: constructing a diffusion model, inputting a high-resolution remote sensing image into a forward Markov chain, adding noise, and generating a low-resolution noise graph aligned with a low-resolution remote sensing image; inputting the low-resolution remote sensing image into a reverse Markov chain based on Swindow-UNet for denoising to generate a high-resolution remote sensing image; adopting a fast diffusion mechanism based on high-level feature skipping and multiplexing to accelerate reasoning in the Swinin-UNet; and restoring a new low-resolution remote sensing image by using an image restoration preprocessing module based on a degradation kernel, and inputting the restored low-resolution remote sensing image into the trained reverse Markov chain based on the Swindow-UNet for remote sensing image super-resolution reconstruction. The method can effectively improve the image resolution, gives consideration to the processing efficiency, and can be widely applied to the fields of satellite image analysis, geographic information systems, environment monitoring and the like.

The invention discloses an image alignment fusion method and system based on template matching and GIFNet, and a medium. The method comprises the following steps: acquiring an infrared image and a visible light image in the same scene, and preprocessing the infrared image and the visible light image; high-precision aligned infrared and visible light images are obtained through a template matching method; inputting the aligned image into a preset GIFNet neural network model, extracting multi-dimensional features through an infrared feature extraction branch, a visible light feature extraction branch and a cross-modal interaction branch, and performing modal feature correlation analysis to dynamically adjust feature extraction emphasis; a feature fusion layer of a GIFNet neural network model is combined with a cross fusion gating mechanism, channel splicing and adaptive weighted summation are carried out on a shallow fusion layer, cross-level fusion is carried out on a deep fusion layer, multi-layer features are fused on a reconstruction branch layer, multi-loss function joint optimization is carried out, and finally a high-quality fusion image is output. According to the invention, accurate alignment and depth feature fusion of the infrared image and the visible light image can be realized.

The invention discloses an anomaly detection method and system based on image alignment and feature difference analysis, and relates to the technical field of computer vision and industrial automation detection, and the method comprises the steps: obtaining a to-be-detected image and a reference image, and carrying out the image alignment processing of the to-be-detected image and the reference image; obtaining a region of interest corresponding to the reference image, wherein the region of interest is a preset region; cutting in the aligned to-be-detected image according to the region of interest to obtain a local image corresponding to the region of interest; obtaining a reference feature corresponding to the region of interest, wherein the reference feature comes from a pre-established reference feature library; extracting features of the local image as to-be-detected features; and comparing the to-be-detected feature with the reference feature to obtain a comparison result, and judging whether the target part existing in the local image is abnormal or not. The problem of dependence of offset interference and abnormal samples is solved, and the method is adaptive to multiple industrial scenes.

The invention discloses a template matching defect detection method and system for BC battery piece silk-screen printing, belongs to the technical field of defect detection, and aims at solving the problems that an existing detection method is weak in pertinence and low in positioning precision, and the general trend of defects is difficult to recognize. The method comprises the following steps: acquiring a template image, marking a calibration point and constructing a calibration data set; generating a functional area mask for the functional area of the template image, and obtaining a functional area template sub-image; an observation image is collected and cut into observation sub-images, calibration points are matched, an affine mapping matrix is generated and corrected, and a calibration observation image aligned with the template image is obtained; comparing the calibration observation image with the template image to generate an effective difference image, and determining an abnormal region; and combining a defect feature library to identify defect types, dividing grades, and retrieving abnormal functional area observation sub-graphs to judge defect general trends. According to the invention, accurate positioning, classification and trend identification of BC battery piece printing defects are realized, the detection efficiency and reliability are improved, and data support is provided for production optimization.