2932 results about "High resolution" patented technology

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.

Luminance information and luminance change information are obtained at the same timing with high resolution. In one example, a solid-state imaging device includes pixels and a control circuit. Each of the pixels includes a photoelectric conversion element that generates an electrical signal based upon incident light, a first pixel circuit that converts the electrical signal into first information, and a second pixel circuit that converts the electrical signal into second information. The control circuit controls each of the pixels such that the photoelectric conversion element is connected to either the first pixel circuit or the second pixel circuit.

Embodiments of the present application provide a three-dimensional model reconstruction and image generation method, a device, a storage medium, and a program product. In the method, multi-stage three-dimensional reconstruction is performed on the basis of a single image of a target object; in a first stage, a plurality of view images are generated on the basis of an image generation model, and an initial three-dimensional model is reconstructed on the basis of the plurality of view images; and in a second stage, on the basis of the plurality of view images and an initial prompt containing set marker information, a text-to-image model is used to learn an association relationship between the target object and the set marker information, and a plurality of scene images are generated on this basis. Compared with the plurality of view images, the scene images generated in the second stage have higher resolution and richer image details, and then the initial three-dimensional model is optimized on the basis of the plurality of scene images, so that a target three-dimensional model having higher resolution and clearer model details can be obtained, thereby paving the way for practical application of a three-dimensional reconstruction solution based on a single image.

The invention discloses a quality detection method in a functionally graded material preparation process, and relates to the technical field of functionally graded materials.The quality detection method comprises the following steps that the temperature of a green body in the functionally graded material preparation process is collected, and green body temperature distribution data is obtained; performing real-time thickness measurement on the green body according to the green body temperature distribution data to obtain a green body thickness change curve; identifying an abnormal fluctuation section in the thickness change curve of the green body, and carrying out phase detection on a physical region with the abnormal fluctuation section of the green body to obtain regional phase composition data; performing internal defect scanning on the green body according to the regional phase composition data to obtain an internal defect map of the green body; performing quality evaluation on the green body based on the green body internal defect map to obtain a quality grade judgment result; according to the method, the ultrasonic flaw detection scanning range is guided based on the phase abnormal region, and high-resolution internal flaw detection can be performed on the high-risk region of the functionally graded material in a targeted manner.

The invention discloses a method for clearly imaging a mask under an optical objective, and relates to the technical field of optical detection and image processing, and the method comprises the following steps: S1, constructing a light intensity distribution model based on the boundary condition of the field of view of the optical objective in combination with the reflection characteristic and incident angle change rule of a metal edge material, and determining the coverage range of metal edge reflection crosstalk, generating a mask boundary interference prediction map; s2, according to the mask boundary interference prediction map, performing region division on the micro-reflectivity image, extracting brightness gradient characteristics of reflectivity lifting in an interference region, and generating a brightness gradient parameter set required by image filtering; the method is based on light intensity modeling, fusion direction filtering, pixel stripping, gradient reconstruction and credibility weighted fusion, realizes closed-loop control from interference prediction to pixel restoration, has high resolution and adaptivity, can accurately strip edge reflection artifacts and restore a real image structure, remarkably reduces misjudgment and rework rate, and is suitable for large-scale popularization and application. And the mask yield and the stability of the detection system are improved.

The invention relates to an ultrasonic phased array full-focusing imaging method for acoustic wave multi-path propagation compensation of a layered composite material, which is characterized by comprising the following steps of: arranging an ultrasonic phased array probe with N array elements on a composite material piece to be detected, obtaining complete echo data by taking each array element as a transmitting array element and taking all array elements as receiving array elements in sequence in a full-matrix acquisition mode; a two-dimensional pixel grid is established in an imaging area, a Monte-Carlo perturbation mechanism used for representing the acoustic wave propagation uncertainty in the layered composite material is introduced into a propagation model for each emission array element-receiving array element-pixel point combination, and the acoustic beam emission direction, the propagation path and the equivalent acoustic velocity are subjected to random perturbation, so that the acoustic wave propagation uncertainty in the layered composite material can be represented. Generating a plurality of equivalent sound wave propagation paths, calculating two-way propagation time of each path, and performing weighted statistics on energy contributions of different paths according to array element sound beam directivity to obtain equivalent propagation time delays of pixel points; and performing delay correction on a full-matrix echo signal by using the equivalent propagation time delay, and performing coherent superposition on signals of all transmitting-receiving array element combinations to realize dynamic focusing of a full-pixel grid, and finally reconstructing an ultrasonic full-focusing imaging image with high resolution and high signal-to-noise ratio. The time delay error caused by propagation path deviation and sound velocity non-uniformity in the layered composite material can be effectively compensated, the focusing precision and spatial resolution of full-focusing imaging are improved, meanwhile, an existing TFM imaging frame does not need to be changed, and the method has good engineering implementability and popularization and application value.

Development of resists are useful, for example, to form a patterning mask in the context of high-resolution patterning. Development can be accomplished using a halide-containing chemistry such as a hydrogen halide. A metal-containing resist film may be deposited on a semiconductor substrate using a dry or wet deposition technique. The resist film may be an EUV-sensitive organo-metal oxide or organo-metal-containing thin film resist. After exposure, the photopatterned metal-containing resist is developed using wet or dry development.

The invention provides a carbon fiber composite material microcrack image segmentation method based on a two-stage super-resolution dynamic attention network, and the method comprises the steps: collecting a microscopic image of a to-be-detected carbon fiber composite material, and carrying out the preprocessing of the image, so as to improve the discrimination between a crack and a background; scanning the preprocessed image by adopting a multi-scale sliding window, carrying out microcrack feature extraction in combination with morphological Top-hat transformation, and positioning a candidate region with microcracks; cutting an image corresponding to the candidate region into sub-images, inputting the sub-images into a pre-trained super-resolution reconstruction network, and amplifying the sub-images to 2-8 times of the original size to obtain a high-resolution candidate region image; inputting the super-resolution enhanced candidate region image into a pre-trained convolutional neural network model for pixel-level crack segmentation to obtain a segmentation result of cracks in the candidate region; and mapping the segmented micro-crack region back to an original image coordinate system, marking the position and shape of the crack on the original image, and outputting a final micro-crack segmentation result.

The invention provides an optical and acoustic image registration method for underwater structure crack detection, and the method comprises the steps: collecting a multi-mode image through the vision field and time synchronization, building a one-to-one correspondence relation according to the pose and time metadata, and improving the image quality through the denoising, distortion correction and other means; optical and acoustic multi-scale features are respectively extracted through a dual-channel network, and cross-modal semantic alignment is realized in combination with a shared weight and a channel attention mechanism; generating a high-resolution scale offset field by using local cross-correlation and micro-upsampling, and introducing manifold regularization constraint to ensure that a vector field is smooth and continuous; according to the method, the local scale field and the affine parameters are combined, non-rigid space mapping is achieved through thin-plate spline interpolation, feedback iterative optimization based on edge structure consistency is assisted, the registration precision of a key structure is enhanced, the automatic registration effect of the underwater multi-modal image is improved, and the method has high robustness and practical application value.

The invention provides a small target detection method and device based on a high-resolution fusion feature map. The method comprises the following steps: dividing an image to be detected into a plurality of overlapped sub-regions and recording position information of the overlapped sub-regions; performing feature extraction and multi-scale fusion on each sub-region to generate a high-resolution fusion feature map; generating geometric parameters and initial confidence of candidate frames based on each spatial position on the image, and performing preliminary screening to obtain a candidate frame set of the sub-regions; mapping candidate frames in all the candidate frame sets to a global coordinate system according to the position information, and combining repeated candidate frames pointing to the same small target to form a global candidate frame set; for each candidate frame in the global candidate frame set, cutting a local image area in the to-be-detected image to carry out fine judgment to obtain a fine trimming confidence coefficient; and fusing the initial confidence coefficient and the refined confidence coefficient to obtain a comprehensive confidence coefficient, and outputting a final small target detection result according to a comparison result of the comprehensive confidence coefficient and an adaptive threshold value. According to the invention, the tiny target in the image can be accurately captured.

The invention discloses a method for optimizing micro-crack segmentation based on deep learning and super-resolution reconstruction, and the method comprises the steps: cutting an image in real time, obtaining an image block which takes a component as a target main body, and synchronously recording a homography matrix for geometric mapping; selecting an amplification strategy to improve the resolution, and recording a scale mapping relation; inputting the enhanced image block into a double-flow network; adaptive fusion and reconstruction are carried out on the two branch features, and a high-resolution texture image is output; generating a geometrically corrected ortho-image, fusing the geometrically corrected ortho-image with original illumination information, and outputting a corrected image with a known pixel size; identifying cracks, spalling and honeycomb diseases in parallel; generating a unified defect confidence map; calculating real geometric parameters of the BIM in a BIM global coordinate system through coordinate back projection; and generating quantitative defect reports and maintenance suggestions. The method has the advantage that seamless connection between the detection result and the BIM global coordinates is realized.

The invention discloses a multispectral image panchromatic sharpening method based on plug-and-play gradient feature guidance fusion, through effective extraction and integration of gradient guidance features (GRAD), excellent recovery results are obtained in the aspects of structural fidelity, detail retention and spectral consistency, and in addition, the method has the advantages of being simple in structure and convenient to use. Gradient feature guidance is fused and designed into a highly flexible plug-and-play method, and the existing architecture can be seamlessly enhanced. In particular, the present invention explicitly models a GRAD by integrating fine spatial details of a high resolution PAN with intrinsic spectral information of an MSI. Meanwhile, an attention mechanism and learnable weighting and residual connection are adopted, and selective aggregation and adaptive fusion of image information are realized, so that the MSI quality is remarkably improved. In addition, the GRAD is systematically analyzed from the aspects of structure, detail and spectrum, and the result is optimized through a multi-objective loss function. In this way, gradient information is fully mined, high-frequency details are captured, cross-modal structure guide alignment is achieved, panchromatic sharpening of the multispectral image is flexibly and efficiently achieved, and the panchromatic sharpening performance is improved.

The invention discloses a building engineering multi-dimensional monitoring system based on panoramic imaging, and relates to the technical field of building engineering monitoring and sensing. Comprising a panoramic image acquisition module, an image splicing and panoramic reconstruction module, a multi-source data synchronous acquisition module, an image-data fusion construction module, an image-physical association modeling module and a risk identification and intelligent response module, an image acquisition array formed by high-resolution cameras is installed at key positions of a building structure, images of all the cameras are obtained, and an original image sequence covering a monitoring area is output. According to the invention, through fusion of the panoramic image and the multi-source sensing data, visualization, intelligent identification and risk prediction of the structure state are realized, a closed-loop monitoring system is constructed, the problems of single traditional monitoring means, data splitting and the like are solved, and the safety monitoring intelligent level of constructional engineering is improved.

The invention relates to the field of environmental monitoring, and particularly discloses a non-negative matrix factorization and adaptive peak recognition fluorescence feature extraction and traceability system, which comprises a spectral data preprocessing module, a spectral data non-negative matrix factorization module, a component number automatic selection module, an adaptive peak recognition module, a feature library construction module and a similarity comparison module. An improved non-negative matrix factorization model is adopted to decompose the three-dimensional fluorescence spectrum matrix of a single sample, and an optimal component number K is automatically determined through multiplicative update rule iterative optimization; the self-adaptive peak identification module carries out selective filtering, accurately extracts the position and intensity of a fluorescence peak through multiple mechanisms, and carries out peak position calibration in a neighborhood; the Hungary algorithm is adopted to carry out characteristic peak matching to calculate the comprehensive similarity between the samples, and rapid and accurate identification of the pollution source is realized. The method has the advantages of high resolution, strong anti-interference capability, low requirement on the number of samples, automation and the like, and is suitable for water quality fingerprint feature extraction of a water sample in a complex environment and real-time source tracing of sewage.

The invention discloses a retina thickness prediction method and system based on a multi-modal image, and the method and system achieve the effective estimation of the retina thickness under a low-cost condition through feature alignment and fusion modeling, and improve the basic screening and follow-up visit capability. According to the invention, through fusion of the multi-mode retina image data, the structure and function information of the optic nerve can be more comprehensively obtained, and the prediction accuracy of the thickness of the retina nerve fiber layer (RNFL) is improved. The OCT high-resolution hierarchical structure and the wide-view texture features of the eye fundus image are combined, so that anatomy and pathological states of optic nerves can be truly restored. The method can be used as an auxiliary method for early screening and early warning of optic neurodegenerative diseases such as glaucoma, and provides support for low-cost and high-efficiency primary screening and clinical auxiliary decision making.

The present invention provides a context-enhanced image generation method, and a model training method and system, comprising: acquiring a training image data set; using a preset noise-adding mechanism to perform multi-timestep noise-adding on a training image, wherein a variance of Gaussian noise added at each timestep depends on a current timestep and progressively increases until a clean training image is transformed into standard Gaussian noise so as to obtain a noisy image at each timestep; using a random masking mechanism to generate a mask, and according to the mask, discarding a masked region of the noisy image and discarding an unmasked region of the clean image; and on the basis of reconstructing a discarded region using a non-discarded noisy image and the clean image, training a context-enhanced image generation model so as to obtain a trained image generation model. The present invention effectively improves the context comprehension capabilities of image generation methods, improves image generation quality, and achieves high-resolution diversified image generation.

Development of resists are useful, for example, to form a patterning mask in the context of high-resolution patterning. Development can be accomplished using a halide-containing chemistry such as a hydrogen halide. A metal-containing resist film may be deposited on a semiconductor substrate using a dry or wet deposition technique. The resist film may be an EUV-sensitive organo-metal oxide or organo-metal-containing thin film resist. After exposure, the photopatterned metal-containing resist is developed using wet or dry development.

The invention discloses a medical image segmentation method based on a lightweight visual basis model, and the method comprises the steps: achieving the cross-modal and cross-anatomical region universal feature extraction through introducing a multi-scale token aggregation and a lightweight decoder; interlayer tokens are fused in a layered mode, global semantics and local textures are considered, and small target and boundary precision is remarkably improved. According to the multi-stage field adaptive pre-training, firstly, a fine-grained structure is captured by self-distillation, reconstruction and regularization composite loss, then, teacher-student distribution is aligned by Gram matrix anchoring loss, and finally, field migration and representation enhancement under the non-labeling condition are realized through high-resolution data refining. The lightweight compression selectively removes part of self-attention and retains MLP, and reduces parameter quantity and calculation quantity on the premise of almost no precision loss, so that the model can perform real-time reasoning on edge equipment. The end-to-end process reduces the annotation dependence, shortens the fine adjustment period, can support multi-organ and multi-focus segmentation through a unified frame, and improves the clinical deployment efficiency and generalization ability.

The invention discloses a high-resolution and low-delay audio and video transmission method and system, which realize high-quality and low-delay remote audio and video transmission by dynamically negotiating transmission resolution and self-adaptive compression coding. The method comprises the following steps: constructing a resolution negotiation matrix through extended display identification data based on receiving end equipment, and determining an optimal transmission resolution parameter; the compression ratio is dynamically adjusted in combination with network bandwidth fluctuation, a compressed data stream is generated by adopting inter-frame prediction coding, and the anti-interference capability is enhanced through forward error correction and time division multiplexing packaging; and finally, the differential signal pair is transmitted to a receiving end to be decoded and restored into a standard audio and video signal. According to the method, the transmission delay is remarkably reduced while the high-resolution image quality is ensured, the method effectively adapts to a complex network environment, the transmission distance and stability are considered, and the method is suitable for application scenes such as remote conferences and real-time monitoring which have high real-time requirements.

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 food waste detection method and system based on image processing, belongs to the field of image recognition, and aims to realize efficient and automatic recognition and quantification of kitchen waste. According to the method, residual food images are collected at multiple periods and multiple angles in a kitchen garbage can or a dinner plate recovery area through high-resolution and multi-spectral imaging equipment, preprocessing is carried out in combination with an improved Retinex algorithm and a space self-adaptive denoising technology, and the image quality is improved. Afterwards, fine segmentation of a food area is achieved through a multi-scale super-pixel segmentation and graph segmentation algorithm, and multi-category intelligent recognition is conducted on remaining food through a recognition network fused with multi-modal features. The system further combines stereoscopic vision and Monte Carlo sampling to dynamically and accurately count the volume or weight of various residual foods. The method has the advantages of high adaptability and accurate statistical result, and can provide data support for catering management, resource recovery, nutrition evaluation and the like.

The invention discloses an image processing method and device for data stream recombination, a medium and a product. The method comprises the following steps: acquiring an original image data stream, and determining row coordinates and column coordinates of each pixel datum in a preset pixel block; according to the row coordinates and the column coordinates, generating a corresponding write-in cache address for each piece of pixel data; writing each piece of pixel data into a target cache unit in the plurality of preset cache units according to the write-in cache address; generating a read cache address according to the global read row count value and the global read column count value; reading the pixel data from the target cache unit and aggregating the pixel data into a recombined pixel row; writing the recombined pixel rows into a continuous storage area in a preset memory to obtain recombined image data; and sequentially reading the recombined image data from the continuous storage area to obtain a recombined image video stream. By implementing the technical scheme provided by the invention, the overall throughput rate of data processing is improved, and the method is particularly suitable for real-time processing of high-resolution and high-frame-rate video streams.

The invention discloses a small target detection method for an Internet of Things video and a related device, and relates to the technical field of Internet of Things and big data, and the method comprises the steps: carrying out the global detection of a to-be-detected Internet of Things video through employing a global-local target detection network, and obtaining a global detection result; determining a target dense region from the global detection result by adopting a target dense region extraction network, and performing super-resolution processing on the target dense region to obtain a high-resolution local image; performing fine detection on the high-resolution local image by using a global-local target detection network to obtain a local detection result; and finally, fusing the global detection result and the local detection result to obtain a high-precision small target detection result. On the basis of global detection of the to-be-detected internet-of-things video, refined local detection is performed on the global detection result, and finally the two detection results are fused, so that the situation of missing detection or false detection is effectively avoided, and the detection precision of dense small target detection in the to-be-detected internet-of-things video can be greatly improved.

The present inventive concept relates to a method and a device for training a machine learning model to construct a digital color image depicting a sample. The method comprising: acquiring a training set of digital images of a training sample by: illuminating, by a plurality of white light emitting diodes, the training sample with a plurality of illumination patterns, and capturing, for each illumination pattern of the plurality of illumination patterns, a digital image of the training sample; receiving a ground truth comprising a high-resolution digital color image of the training sample, wherein a resolution of the high-resolution digital color image is relatively higher than a resolution of at least one digital image of the training set of digital images; and training the machine learning model to construct the digital color image depicting a sample using the training set of digital images and the ground truth. The present inventive concept further relates to a microscope system and a method for constructing a digital color image depicting a sample.

The invention relates to the technical field of photoresist, in particular to a color photoresist spraying and developing process for a high-resolution display panel, which comprises the following steps: S1, preparing composite color photoresist; s2, substrate pretreatment; s3, a spraying process; s4, pre-baking is carried out; s5, exposure is carried out; s6, developing; s7, carrying out post-baking; by optimizing the components and process parameters of the color photoresist, the core problems of difficult high-resolution forming, poor film adhesion and insufficient solvent resistance in the traditional process are effectively solved: on one hand, the modified resin and the fluorine-containing monomer are combined and matched with the plasma substrate for pretreatment, so that the adhesion grade of the photoresist film and the substrate reaches 0 grade; the solvent wiping resistance is more than 300 times, so that the long-term use reliability requirement of a terminal product is met; and on the other hand, spraying, pre-baking, exposure and developing parameters are synergistically regulated and controlled, and a stable color paste dispersion system is combined, so that high-resolution pattern forming greater than or equal to 590PPI is realized.

The invention discloses a large-view-field high-resolution imaging system based on polarization regulation and control, the large-view-field high-resolution imaging system comprises a metasurface system array and a camera along the direction of a light path, and the metasurface system array comprises a plurality of pairs of metasurface systems which are distributed in an array; each pair of metasurface systems comprises a first metasurface system and a second metasurface system; the first metasurface system is used for performing high-resolution imaging and large-view-field imaging on a first polarization light beam and a second polarization light beam of an incident light beam respectively; the second metasurface system is used for performing large-view-field imaging and high-resolution imaging on the first polarization-state light beam and the second polarization-state light beam respectively; the camera is used for acquiring and fusing images formed by different metasurface systems so as to capture a high-resolution central region of interest and surrounding environment information of a wide field of view. The system can efficiently realize higher imaging quality.

The invention belongs to the technical field of medical diagnosis, and discloses a quantum multicolor immune digital pathological diagnosis analysis system, which comprises the following steps: acquiring high-resolution digital images of an immunohistochemical or immunofluorescence slice and a slide, and analyzing multicolor signal distribution in the images to obtain a quantum chromatographic image set; pixel-level color separation and intensity quantization are carried out on the quantum chromatographic image set, and a quantum intensity index set of each pixel is generated; performing multi-target color overlay analysis and post-translational modification protein specificity evaluation on the quantum intensity index set to construct a multi-color modification index set; identifying a single cell boundary and a multi-cell community based on the multicolor modification index set, and extracting a cell morphological parameter and a spatial adjacency relation to obtain a cell quantum analysis set; performing consistency correction and traceable recording by applying an automatic quality control mechanism, and generating a digital diagnosis report; the accuracy of accurate qualitative and quantitative analysis of immunohistochemistry and immunofluorescence is greatly improved.

The invention relates to the technical field of medical instrument quality detection, in particular to a medical instrument intelligent defect detection method and system based on image processing. The method comprises the following steps: acquiring a multi-angle image through a high-resolution industrial camera, and adaptively adjusting illumination and camera parameters to realize image standardization; the image is divided into local Patch, and abnormal sensitive mapping is generated through multi-scale convolution filtering, feature enhancement and fusion; generating candidate defect areas through dynamic scoring, neighborhood aggregation, boundary optimization and multi-index layering based on the mapping; carrying out defect confirmation and grading by adopting multi-model cross validation and dynamic weight fusion; historical data and environmental factors are combined to predict a defect trend through an autoregression model, and a detection strategy is dynamically adjusted to realize closed-loop optimization. According to the method, the accuracy, robustness and efficiency of small defect detection are improved, and the method has adaptive and predictive capabilities.

The invention relates to the technical field of electron energy measurement, and discloses an electron spectrometer and an electron energy spectrum measurement method and system.The electron spectrometer comprises a magnetic deflection unit used for receiving an incident to-be-measured electron beam, generating a magnetic field and applying the magnetic field to all to-be-measured electrons in the to-be-measured electron beam; the scintillator fluorescent screen is arranged at the downstream of the magnetic deflection unit and used for receiving the deflected electrons to be detected, and the electrons bombard the scintillator fluorescent screen and generate light spots; the optical acquisition unit is used for acquiring a light spot image on the scintillator fluorescent screen; and the signal processing unit is used for processing the light spot image so as to identify the spatial position information of the light spot in the light spot image and convert the spatial position information into electronic energy data. The method has the beneficial effects that the unification of high resolution and high measurement efficiency is realized, and the problem of signal accumulation under a high counting rate of a traditional direct measurement method and the inherent defect of low efficiency of a scanning type magnetic spectrometer are overcome.

A method of inspecting a Fiber Array Unit (FAU) in a co-packaged optics application using an inspection apparatus, includes: Positioning the FAU in the apparatus. Utilizing a high-resolution CCD imaging system to capture detailed images of the FAU. Employing an IR CCD to assess the core pitch position of the FAU. Measuring the quality of output beams from the FAU's optical fibers using a beam profiler. Actively aligning the FAU using left and right 6-axis alignment units.