5249 results about "High resolution" patented technology

The invention discloses a visible light and infrared image fusion method based on cross-modal dynamic collaboration. The method comprises the following steps: respectively extracting texture detail features of a visible light image and thermal radiation features of an infrared image through a visible light encoder and an infrared encoder; spatial alignment and channel complementarity optimization of cross-modal features are realized by using a heterogeneous attention collaboration module; and performing layered fusion on deep semantics and shallow detail features through a dynamic gating multi-scale decoder to generate a high-resolution fusion image. According to the method, the problems of feature dislocation, detail loss and unreasonable fusion weight distribution caused by modal difference in the prior art are solved, the detail fidelity, the thermal target saliency and the complex scene adaptability of the fusion image can be remarkably improved, and a high-robustness fusion result is provided for low-illumination environment perception and multi-modal target recognition.

The invention discloses a glass lens surface scratch detection method and system, relates to the technical field of precision optical detection, and aims to solve the problems of scratch false detection, leak detection and poor algorithm adaptability caused by interference fringes, noise coupling and poor form adaptability in a high-reflection / complex coating process scene in the prior art. According to the scheme, an orthogonal polarization state composite light field is generated based on a multi-angle polarization light source array and a near-infrared compensation light source, and candidate regions are extracted through dynamic threshold segmentation and a direction gradient tensor matrix; gaussian pyramid multi-scale feature fusion and refraction angle consistency verification are utilized to eliminate artifact interference; constructing a direction constraint convolution kernel group to decompose scratches and background textures, and dynamically allocating computing resources in combination with a cascade network; feeding back closed-loop calibration light source wavelength and convolution kernel parameters in real time through coating parameters; according to the method, the precision and robustness of high-reflectivity surface scratch detection are remarkably improved, and meanwhile, the requirements for high-resolution image processing and real-time performance in a high-speed production line are balanced.

According to the PCB production line defect detection system and method based on image processing, a plurality of high-resolution cameras, an infrared thermal imaging camera and a 3D laser scanner are adopted, a dynamically-controlled annular LED light source, polarized light illumination and a structured light projection technology are combined, a foreground and a background are separated through a self-adaptive threshold Otsu method, and the defect detection accuracy is improved. A convolutional neural network denoising model and a frequency domain filtering technology are introduced, welding spot and line features are extracted from a preprocessed image, a coding scheme is designed, multi-modal features are fused, spatial-temporal feature analysis is introduced, fine classification and positioning are performed in combination with SVM preliminary screening and a YOLOv5 deep learning model, an online learning mechanism is introduced, model parameters are dynamically optimized, and a multi-modal image is obtained. An intelligent early warning mechanism is established, automatic grading and alarm sending are carried out, a statistical report is automatically generated, production process parameters are recorded, root cause analysis is carried out in combination with big data analysis, and potential defects are early warned in advance.

The invention provides a square power battery shell visual defect rapid detection method and system, and relates to the technical field of image processing, and the method comprises the steps: obtaining a high-resolution multi-view image sequence, outputting standardized image data, carrying out front visual scratch detection, outputting scratch defect positions and features, and outputting defect candidate regions containing confidence scores; and recognizing bubble defects, summarizing recognition results of the defect candidate areas, and generating a labeling image. The method solves the problems that in the prior art, due to the fact that multi-view image information is not fully fused, traditional scratch detection depends on a single edge feature, and a main defect recognition model is insufficient in recognition capacity for tiny defects such as bubbles, defect detection accuracy is low, missing detection and false detection phenomena are serious, and detection efficiency is high. According to the method, the problems that the defect is difficult to comprehensively and accurately locate and classify in the prior art are solved, the multi-view information fusion capability and the identification accuracy and detection efficiency of multi-class defects are improved, and high-precision, comprehensive and real-time quality monitoring of defect detection results is realized.

According to the medical image segmentation method and device based on spatial perception and frequency domain information, the precision and robustness of medical image segmentation are effectively improved by combining frequency domain information guidance and a multi-head state spatial perception technology. Frequency domain transformation is performed on a medical image, high-frequency and low-frequency components of the image are separated by using a multi-scale decomposition technology, and low-frequency features are extracted to obtain global information. By introducing a learnable noise filtering mechanism, noise and irrelevant background information in a frequency domain are suppressed, so that the model can be focused on a lesion area more accurately. A multi-head perception visual state space module is designed on a bottleneck layer, lesion features of different scales are captured through a multi-scale adaptive feature fusion mechanism, and the capability of segmenting small-size lesions and complex structures is enhanced. A context focusing attention mechanism is introduced into jump connection, fusion of global information and local details is further enhanced, and the accuracy of a segmentation result is ensured; and finally, recovering a high-resolution segmented image through a decoder.

The invention relates to the technical field of industrial automatic quality inspection, and provides a PCBA surface defect detection method and system based on deep learning and a medium, and the method is used for carrying out defect detection on a preset PCBA board. Comprising the following steps: acquiring a surface image of a PCBA board according to a preset multi-angle light source and a high-resolution camera, and performing adaptive illumination compensation and noise removal processing on the surface image to generate a standardized image; performing multi-scale segmentation on the standardized image to obtain image blocks including local details and a global structure; constructing a double-branch deep learning model, wherein the double-branch deep learning model comprises a backbone network, a multi-scale feature fusion module and a defect detection branch; inputting the image blocks into a double-branch deep learning model, and outputting a thermodynamic diagram and probability distribution by the double-branch deep learning model; performing binarization processing on the thermodynamic diagram by using a dynamic threshold segmentation algorithm to generate a defect mask; and outputting a defect detection result of the PCBA board according to the defect mask and the probability distribution, and completing the defect detection of the PCBA board.

The invention discloses a large-format image segmentation method and system fusing frequency domain perception and state space modeling. According to the method, three core parts including an encoder, a state space modeling module and a frequency domain perception feature fusion module are included, a feature extraction network based on a ResNet structure is adopted by the encoder, multi-scale feature extraction of an input image is achieved, and effective capture of deep semantic information is ensured. The state space module realizes adaptive optimization of image data through visual state space modeling, and constructs a hybrid architecture in combination with a wavelet multi-scale convolutional network, thereby effectively solving the limitation of the traditional CNN in the aspect of remote dependence capture. And the frequency domain perception feature fusion module effectively optimizes the feature reconstruction process through interactive fusion of multi-frequency domain information, so that efficient and fine pixel-level semantic segmentation is realized. The decoder can integrate a low-frequency global structure and high-frequency edge detail information, and the recovery effect of edges and textures in a high-resolution image is remarkably enhanced.

The invention discloses a hyperspectral image classification method based on proxy attention and a VIT architecture, and belongs to the field of image classification, and the method comprises the following steps: preprocessing hyperspectral image data, and dividing the hyperspectral image data into a training set and a test set; constructing a hyperspectral image classification model based on proxy attention and a VIT architecture; training the hyperspectral image classification model by adopting the training set; and inputting the hyperspectral data in the test set into the trained hyperspectral image classification model, reasoning to obtain a prediction category, and evaluating the obtained prediction category through an evaluation index. According to the invention, the agent attention encoder based on agent attention is constructed, hierarchical feature extraction is carried out through the agent attention encoder of L-layer cascade, layer-by-layer fusion and enhancement of space-spectrum information in an initial feature coding matrix are realized, the calculation complexity is significantly reduced while the strong representation capability is maintained, and the method has the advantages of high efficiency, high resolution and the like. And good balance between the calculation efficiency and the feature expression capability is realized.

The invention provides a multi-modal document understanding model, a training method, a reasoning method and equipment, global visual features are extracted by using a weight-frozen first visual encoder, the understanding ability of the model to natural scene images is enhanced, a second visual encoder extracts fine-grained features based on high-resolution document images and region-of-interest information, and the understanding ability of the model to natural scene images is enhanced. And the analysis precision of the complex document is improved. And the information interaction module improves the intelligent understanding ability of a specific area in combination with the position of the region of interest input by the user. The feature fusion module splices multi-modal features in a channel dimension, so that visual information from different sources is efficiently integrated. The linear layer converts feature dimensions, so that the visual features are adaptive to the input requirements of the large language model, and the large language model combines visual and text information to generate a text understanding result conforming to semantic logic. According to the model, the capability of analyzing and extracting the fine granularity of the document information is improved by combining the two-way visual encoder with the selection of the region of interest of the user on the document image of the image-text structure.

The invention relates to the technical field of shock absorber defect detection, and discloses an electric vehicle shock absorber defect detection method and system based on machine vision, and the method comprises the steps: collecting an initial image set under the irradiation of a multi-angle light source through high-resolution imaging collection equipment; performing denoising and filtering processing according to the initial image set to obtain clear image data; performing defect classification and spatial distribution analysis according to the clear image data to obtain surface feature vectors containing defect types and defect spatial distribution; carrying out vibration amplitude acquisition and phase angle measurement operation according to the surface feature vector, and carrying out spectral analysis to construct a performance parameter vector; performing data fusion according to the performance parameter vector and the surface feature vector to obtain a fusion feature; inputting the fusion features into a pre-constructed association prediction model to obtain defect prediction data; and performing defect influence degree analysis according to the defect prediction data to obtain a defect evaluation result. The method provides a basis for quality control and performance optimization of the shock absorber.

The invention provides a multi-layer optical film detection method, device and equipment and a storage medium, and the method comprises the steps: obtaining a polarization phase difference signal of transmission light and reflected light through multi-angle incidence irradiation of a multi-band programmable laser source, and recording the polarization ellipticity parameter change; then combining confocal microscopic interference and polarization analysis to determine a space coordinate and a stress distribution diagram of a stress abnormal region; continuously irradiating in a photo-thermal coupling environment through a broadband light source, and monitoring interference signals, polarization states and temperature changes to obtain dynamic evolution characteristics; and finally, performing a simulation test on the multi-layer optical film in a system environment with preset optical axis arrangement and light beam energy distribution, obtaining an in-situ polarization state and interference pattern data, and evaluating the optical performance of the film layer. According to the invention, potential defects can be positioned under high resolution, and the reliability and imaging quality of the film layer under complex application conditions can be accurately evaluated.

The invention relates to the technical field of abnormal event early warning, and provides a large-scene monitoring video abnormal event early warning method based on a multi-mode large model. According to the invention, the problems of delay, low accuracy and limited coverage range of abnormal event early warning of large-scene monitoring videos in the prior art are solved. According to the main scheme, multiple paths of high-resolution monitoring videos are spliced and preprocessed to generate a panoramic video; synchronously acquiring and preprocessing audio and sensor data to construct a multi-modal data set; video key frames are extracted by adopting a traditional small model, and the video key frames and multi-modal data are jointly input into a multi-modal large model based on a Transform architecture for deep feature fusion; abnormal events such as tumble, congestion and fight are identified based on the fusion features; triggering an early warning mechanism to send event type and position information in real time; and storing the full-dimensional data of the abnormal event for tracing analysis. The real-time processing performance is optimized through edge calculation, the complex scene understanding ability is enhanced in combination with a multi-modal large model, and the detection precision and the response speed are remarkably improved.

The invention discloses an intelligent camera system and optimization method based on an image processing algorithm, and relates to the field of camera intelligent systems, and the system comprises an image collection module, a preprocessing module, a feature extraction module, a target recognition module, a decision module, a feedback control module, an illumination compensation module, an anomaly detection module and a data storage and analysis module. The method comprises the following steps: acquiring images in real time through a high-resolution sensor, denoising, enhancing and normalizing the images through preprocessing, generating feature vectors through LBP and HOG algorithms for feature extraction, identifying targets through a CNN model for target identification, generating instructions according to identification results and rules through decision making, adjusting camera parameters according to feedback control, and compensating to improve image quality through illumination compensation. The detection module detects an image abnormal area, and information collected by the data storage and analysis storage analyzes a mode rule. According to the method, clear images are collected under different illumination conditions, features are accurately extracted, targets are identified, intelligent decision is made, parameters are flexibly adjusted, abnormity can be detected, and analysis data can be stored to assist system optimization and upgrading.

The invention provides a packaging test method of an LED flip chip and related equipment. The method comprises the following steps: acquiring wafer packaging parameters of the LED flip chip; determining a warping degree prediction model of wafer packaging parameters and a process parameter sensitivity sequence through multi-physics field coupling modeling; monitoring a warpage measurement distribution diagram of the wafer according to the warpage prediction model; determining wafer warpage compensation data according to the process parameter sensitivity sequence and the warpage measurement distribution diagram; comparing a three-dimensional deformation graph correspondingly generated by the wafer warping degree compensation data with a prediction result of the warping degree prediction model, and determining a comparison result; and performing welding spot defect high-resolution scanning on the wafer of the LED flip chip according to the comparison result, and identifying the welding spot defect of wafer packaging. Through the implementation of the scheme of the invention, accurate prediction, effective compensation and reliable detection of wafer buckling deformation in the packaging process of the LED flip chip can be realized, and the packaging quality is remarkably improved.

Ways to post-process a decoded mesh and modify received triangles per patch to improve the mesh geometry are described herein. Since the transmitted geometry contains the high-resolution surface information, the information is able to be applied to the mesh reconstruction operation to generate triangles that are aligned with the original surface. Methods include generating new triangles by splitting the received triangles' edges according to their size, by inserting new vertices at the triangle's centroids, by splitting the vertices, and by performing marching cubes in surfaces defined by the geometry images.

The invention discloses a packaging box printed matter printing quality detection method, which comprises the following steps of: acquiring a high-resolution printing image through an image acquisition module, preprocessing and segmenting the printing image by an image processing module, optimizing image quality, classifying the preprocessed image by an image classification module according to characters, patterns and color blocks, and detecting the printing quality of a packaging box printed matter. The defect detection module performs multi-dimensional detection on the classified preprocessed images based on image parameter data, the quality analysis module quantitatively evaluates quality scores of characters, patterns and color blocks based on detection results, and the computer terminal generates a comprehensive score through weighting calculation and automatically generates a detection report; according to the packaging box printed matter printing quality detection method, complex defects such as font errors, stroke breakage, pattern deviation, color deviation and stains are effectively recognized through multi-dimensional defects, the manual reinspection cost is reduced through full-process automatic processing, and the quality of printed matter is visually reflected through a quantitative evaluation system.

The invention provides a multi-modal gait recognition method and system, and aims to solve the problems of accuracy and robustness of gait recognition in a complex environment. According to the method, two types of modal information of a gait contour map and a skeleton Gaussian heat map are combined, global-local shallow gait features are extracted by using a multi-scale convolution feature extraction module based on residual connection, and adaptive weight distribution and fusion are performed on the features of the two types of modals through a cross-modal attention fusion module. Furthermore, a sliding window Transform module is introduced to perform deep gait feature extraction on the fused features, and a long-range space-time dependency relationship of gait information is modeled to obtain a gait feature vector with high resolution. Experimental results show that the gait recognition precision of the method in a complex scene is remarkably superior to that of an existing method, and particularly, the gait recognition precision is excellent under extreme conditions of shielding, complex backgrounds and the like. The invention provides a new solution for high-precision gait recognition in an open environment.

The invention provides a multi-modal visual understanding model based on double-path visual coding, a training method, a reasoning method and equipment. The model comprises a first visual encoder used for extracting global visual features of a natural general image and outputting first image features, and weight freezing of the first visual encoder; the input of the second visual encoder is an image of which the size is adjusted to a preset high resolution, and the second visual encoder is used for extracting local detail information of the image and outputting a second image feature; the feature fusion layer is used for splicing the first image feature and the second image feature in a channel dimension to form a fused visual feature; the linear layer is used for converting the fused visual features into input dimensions required by a large language model; the large language model is used for generating natural language answers based on the fused visual features after dimension conversion and text input. According to the method, a double-path visual coding structure is adopted, the image analysis capability of the multi-modal visual understanding model is improved, and the alignment mode of the visual features and the large language model is optimized.

The present invention relates to a system (100) and method (300) for automated product validation, identification, and transaction management in shopping environments. The system (100) integrates a smart shopping cart (102) equipped with at least one camera (106), a load cell (108), and a user interface (110). An AI model trained on a dataset of high-resolution, 360° product images generate unique image embeddings for each product. The system performs multimodal validation by comparing the extracted image embeddings, measured weight data, and scanned barcode information with reference data stored in a vector database. Further, discrepancies, such as unscanned products or mismatches, are flagged, and payment is blocked until resolved.

The invention relates to the technical field of image analysis, in particular to a QFP chip image pyramid positioning system which comprises a pyramid construction module, a pin initial positioning module, a pin accurate positioning module and an interference area elimination module. According to the method, an original chip image is processed through step-by-step down-sampling and Gaussian filtering, the image processing speed is effectively improved when a multi-layer image pyramid is constructed, a pin connected domain is clearly identified by applying binarization processing and a pin neighbor connection strategy, the pin area identification capability of the system is enhanced, and the identification accuracy of the system is improved. Region recalibration is carried out by using an up-sampling image of an original image data size, accurate pin positioning can be executed while high resolution is maintained, the probability of wrong positioning is remarkably reduced by accurately extracting pin contour information and effectively eliminating non-target connected domains, the chip assembling quality is improved, the chip assembling speed is increased, and the chip assembling efficiency is improved. And remarkable benefits are brought to a high-speed electronic assembly line.

The embodiment of the invention provides a multi-modal large model image segmentation method and device based on hierarchical lexical representation, a mask image is coded into a lexical sequence by designing a mask marker, and progressive generation from a shape prototype to local details is realized through a causal attention mechanism. A three-stage training strategy is adopted, firstly, a marker is trained through a mask reconstruction task, then mask lexical elements are integrated into a large-scale multi-modal model and combined training is carried out, and finally fine tuning is carried out by utilizing high-resolution data. And performing multi-level supervision based on a hierarchical mask loss function to realize accurate segmentation of a natural language description target. According to the method, the defects of the traditional technology in the aspects of complex scene understanding, model training, mask generation and the like are effectively overcome, and the performance of multi-modal image segmentation is remarkably improved.

The invention discloses a large-breadth cross-scale adhesive film defect detection method and a large-breadth cross-scale adhesive film defect detection device. The method comprises the following steps: carrying out coarse focusing on a camera and capturing a glue film image, carrying out coarse scanning on the glue film image and extracting information of a printed image-text in the glue film image by using an image difference method, inputting an output difference image into an anchor frame algorithm, inputting an output fine scanning target area coordinate into a genetic algorithm, and carrying out fine scanning on the glue film image. Outputting an optimized fine scanning path in combination with S curve planning characteristics of the motor; the motion platform is driven to complete path execution by combining double-axis linkage control with the fine scanning path, camera focusing is completed in the path execution process, and a high-resolution image of a defect area is collected and registered; and carrying out image splicing and fusion on the registered images by combining an optimal suture line algorithm with an improved Laplacian pyramid fusion method, and carrying out defect detection on the fused complete adhesive film image. The method has the advantages of high splicing precision and fast processing efficiency, and can effectively retain more detail information.

The invention relates to the technical field of cable crimping detection, in particular to a cable crimping detection method and device based on image processing. The method comprises the following steps: acquiring a multi-angle high-resolution monitoring image of a cable crimping point; performing dynamic background elimination processing on the multi-angle high-resolution monitoring image to generate a background-eliminated cable crimping point monitoring image; carrying out image attitude jitter analysis on the background elimination cable crimping point monitoring image one by one angle, and carrying out attitude deviation correction processing to construct an attitude correction monitoring image; cable crimping area feature point identification is carried out on the attitude correction monitoring image, and three-dimensional point cloud modeling is carried out, so that a crimping point three-dimensional registration optimization model is obtained; and carrying out crimping point defect visual detection on the crimping point three-dimensional registration optimization model, carrying out visual rendering, and constructing a crimping point defect form rendering model. According to the invention, high-efficiency and accurate cable crimping detection is realized.

The invention provides a high-imaging-resolution method for identifying a lead sealing defect part of a cable, and relates to the field of nondestructive testing. Comprising the steps that a linear array probe array is utilized, a plurality of array elements emit ultrasonic waves to a to-be-detected defect part at the same time, and a received echo signal reflects the waveform shape and the waveform phase of the defect. A two-dimensional image is preprocessed through a synthetic aperture focusing technology, a combined image is reprocessed through a phase coherence technology, the quality and accuracy of the image are further improved, and a high-resolution three-dimensional image is constructed in combination with an imaging synthetic aperture focusing technology. The ultrasonic phased array technology, the SAFT technology, the phase coherence technology and the synthetic aperture focusing technology are combined, the imaging resolution is effectively improved, the method has remarkable advantages especially when defect parts in complex structures such as cable lead seals are recognized, the imaging quality is optimized, a two-dimensional image is converted into a high-resolution three-dimensional image, and finally an accurate defect recognition result is obtained.

The invention discloses a multispectral image fusion model based on a double-branch self-attention-generative adversarial network and a fusion method thereof, and the model sequentially comprises an input preprocessing module which is used for carrying out the same-amplitude mapping, normalization and overlapping block embedding of visible light and infrared original images, and generating a to-be-fused feature block; the double-branch encoder module captures a cross-modal long-distance dependence and overall brightness structure through multi-head deep convolution transpose attention (MDTA) and a gated deep convolution feedforward network (GDFN), and extracts high-frequency texture and edge information by using a reversible residual gating layer and detail DetailNode iteration; the fusion decoder module is used for carrying out multi-level self-attention-convolution reconstruction on the two paths of features after channel dimension splicing, and outputting a single-frame high-resolution fusion image; and the double-domain discriminator module comprises a visible light domain discriminator and an infrared domain discriminator which are respectively used for carrying out adversarial evaluation on the fused image and the corresponding modal truth value image so as to improve the detail authenticity and the thermal target contrast ratio of the fusion result. The technical problems that an existing infrared-visible light image fusion method is insufficient in detail reservation, unbalanced in brightness and contrast, poor in unsupervised training stability and the like are solved, the method can be deployed on embedded platforms needing real-time and multi-modal information enhancement such as night monitoring, unmanned driving and edge security and protection, and high-contrast and high-information-amount fusion imaging is achieved.

The invention relates to the technical field of image segmentation, in particular to an unmanned aerial vehicle image building extraction method and system fusing DINOv2 and global attention, and the method comprises the steps: constructing a building segmentation network based on DINOv2 and global attention; the building segmentation network comprises an encoder network used for extracting an input image feature map and carrying out encoding output and a decoder network used for decoding the encoding output and segmenting and predicting an input image building, and the encoder network adopts a DINOv2 visual model as a backbone network for extracting the image feature map; the decoder network captures image space semantic information by using a global attention mechanism and a feature enhancement mechanism so as to perform semantic segmentation prediction; and training the building segmentation network based on the target loss function by using the sample data set, so as to segment the unmanned aerial vehicle image building by using the trained model. According to the method, image building features can be more stably obtained, network performance is improved, and building extraction with rich high-resolution unmanned aerial vehicle image semantics and complex background information is met.

The invention provides a fuzzy barcode image processing method and system fused with super-resolution repair. The method comprises the following steps: acquiring a bar code image containing dynamic blurring, detecting a blurred region formed by superposition of motion tracks in the bar code image based on space-time continuity characteristics of the dynamic blurring, extracting an edge diffusion direction of the blurred region, and determining the dynamic blurring of the bar code image according to the edge diffusion direction. And performing multi-scale detail layer generation on the fuzzy region, inputting the multi-scale detail layer into a fusion repair module, and generating a repaired high-resolution barcode image by alternately executing super-resolution reconstruction guided by local high-frequency information and morphological repair of a module gap. Edge sharpening and noise suppression are carried out on the high-resolution barcode image, and a clear image meeting the barcode decoding standard is output; according to the technical scheme provided by the invention, the restoration precision and reliability of the complex dynamic fuzzy scene are remarkably improved, and the output image strictly meets the geometric constraint requirement of bar code decoding.

The invention relates to a method and system for measuring the three-dimensional morphology of a metal hydrophobic surface based on white light microscopic interference, and belongs to the field of precision measurement, and the method comprises the steps: firstly, measuring a sample, scanning the surface of the sample, and synchronously collecting a white light interference image sequence containing surface contour information; adopting a bicubic interpolation algorithm to carry out interpolation processing on the acquired white light interferogram group, and constructing a high-resolution interference image sequence; analyzing the time-frequency domain interference signal through fast Fourier transform, effectively inhibiting noise interference in combination with a filtering technology, and accurately extracting an envelope curve of the interference signal; and carrying out Gaussian fitting on the envelope peak by adopting a least square method and positioning a peak value coordinate, and finally reconstructing a high-precision three-dimensional surface topography. According to the method, through advantage complementation of the bicubic interpolation algorithm and the fast Fourier transform method, the transverse resolution is remarkably improved while the longitudinal resolution is kept, and an effective technical means is provided for accurate characterization of the three-dimensional morphology of the metal hydrophobic surface structure.

The invention discloses an on-line detection method and system for etching quality of a laser perovskite cell, and the method comprises the steps: employing a double-camera alternate working mode, installing high-resolution CCD camera assemblies at two sides of a laser processing head in an X direction, eliminating the shielding of a motion direction, carrying out the scribing quality analysis through combining the submicron optical imaging of the CCD camera assemblies with an image processing algorithm, and carrying out the detection of the etching quality of the laser perovskite cell. According to the method, local adaptive threshold segmentation is put forward, an Otsu method is used for adaptively calculating a binarization threshold, a morphological opening operation is used for removing irrelevant information, an edge searching logic algorithm is used for searching two edges of a scribed line, finally, the two edges and a center line are fitted, and scribed line quality analysis is carried out through the three lines. And real-time power adjustment, position calibration and defect position marking are carried out by detecting the scribing quality, the calculation speed of the high-resolution optical detection system is high, detection can be carried out while machining is carried out, and machining-detection-correction closed-loop control is achieved.

The invention provides an intelligent online analysis and monitoring system and method for circuit board production, and the system comprises an image collection module, a multispectral imaging module, a three-dimensional scanning measurement module, a copper thickness test module, a data collection module, an intelligent analysis and recognition module, and a self-adaptive algorithm optimization module. The system is used for carrying out real-time image acquisition on each key station in the circuit board production process by adopting a high-resolution industrial camera. Through high-definition image acquisition, multispectral imaging, three-dimensional scanning and measurement, comprehensive data acquisition in the circuit board production process is realized, the problem of limited data acquisition range in the prior art is solved, accurate detection and classification of circuit board defects are realized by applying an intelligent analysis algorithm, and the production efficiency is improved. The accuracy and efficiency of defect detection are improved, automatic adjustment and optimization of algorithm parameters are achieved through the self-adaptive algorithm optimization module, it is ensured that the algorithm can continuously adapt to changes of the production environment, and the stability and reliability of the system are improved.