5784 results about "Optical imaging" patented technology

The invention relates to the technical field of optical detection, in particular to an automatic optical detection method and system based on gasket surface defects. The method comprises the following steps: carrying out parameter extraction on a to-be-detected gasket, obtaining inner and outer diameters, thickness and tolerance range information of the gasket, and obtaining gasket structure parameter data; matching, calibrating and storing the gasket structure parameter data according to a preset element parameter library to obtain a gasket element parameter library; performing surface scanning on the to-be-detected gasket by using a visual sensor to generate original image data; performing multi-channel optical imaging processing on the original image data, and performing illumination unevenness compensation by using a high dynamic range technology to obtain enhanced surface image data; and carrying out image preprocessing on the enhanced surface image data to obtain gasket surface detection image data. According to the method, the multi-channel optical imaging and advanced image processing technologies are adopted, the self-adaptive algorithm is combined, tiny defects can be accurately recognized, and the false detection rate and the omission ratio are remarkably reduced.

An intelligent traffic management device for optimizing traffic flow at one or more intersections using reinforcement learning, comprising: a central control unit housed in a weatherproof enclosure and mounted on a structural support near an intersection; a sensor interface operatively connected to a variety of multimodal sensors, including at least radar-based vehicle detectors, inductive loop sensors embedded in the road surface, infrared pedestrian detection detectors, and high-resolution optical imaging modules; a powerful embedded computing unit consisting of a multi-core processor and a GPU configured for on-device inference; a reinforcement learning engine embedded in the computing unit, comprising a deep reinforcement learning model trained to map environmental states defined by traffic-related sensor inputs to optimized action policies for traffic light control; a signal actuation module electrically connected to traffic signal controllers, with action policies outputting signal phase durations and transitions in real time; a communications module supporting wireless connectivity and configured for device-to-device communication and cloud synchronization; a policy synchronization module configured to perform federated learning-based updates of model parameters by exchanging gradient weights or distilled traffic state vectors with neighboring intelligent traffic management devices deployed in a traffic network; where the reinforcement learning engine continuously observes traffic state vectors consisting of vehicle counts, average speeds, queue lengths, and pedestrian activity, and dynamically updates the policy to maximize cumulative traffic throughput and minimize average delay.

The invention belongs to the technical field of optical precision measurement, and particularly relates to a wafer defect detection system based on dual-channel optical imaging. According to the invention, dark field scattering and diffraction phase microscopy are combined as two optical imaging channels; optical paths of the two imaging modes are compatible and synchronized; through optical system calibration and optimization, high-precision acquisition and alignment of multi-modal images are realized; calibrating the sensitivity and resolution of the system by using a standard sample to ensure that the detection performance meets a preset requirement; carrying out dual-channel synchronous imaging on the wafer sample to be detected, and respectively obtaining a dark field scattering image and a diffraction phase image; carrying out conjoint analysis on the multi-modal data through an image algorithm, extracting size, depth and structure information of defects, and generating a high-precision defect detection report; the system specifically comprises a dark field scattering imaging module, a diffraction phase microscopic imaging module and an image processing unit. According to the invention, the defect of a single imaging mode can be effectively overcome, and the detection precision and robustness of the system are remarkably improved.

The invention discloses an AOI optical scheme automatic optimization method based on reinforcement learning, and belongs to the technical field of automatic optical detection. According to the method, a reinforcement learning technology is applied to automatic adjustment of optical schemes in a machine vision system, wafer images under different optical schemes are collected by using an AOI system, and scoring is performed by using a plurality of semantic segmentation models; constructing and training a non-reference image quality evaluation network, evaluating the optical imaging quality in real time, and designing a reward function on the basis; by using the Actor-Critic algorithm, the intelligent agent can autonomously learn an optimal parameter adjustment strategy, quickly adapt to different scenes, realize dynamic optimization of light source parameters and remarkably improve image quality, and an efficient and intelligent solution is provided for efficient application of a machine vision system in a complex industrial environment.

The invention discloses a deep sub-wavelength scatterer particle size evaluation method based on coherent axial diffraction calculation, and belongs to the technical field of optical imaging and microstructure analysis, and the deep sub-wavelength scatterer particle size evaluation method comprises the following steps: obtaining an image, and preprocessing the image; quantifying the preprocessed image, obtaining the scattered light energy concentration of each axial position of an image surface scatterer, obtaining the attenuation condition after scattered light energy defocusing based on the scattered light energy concentration, and compensating defocusing attenuation; the method comprises the following steps: extracting a peak value position and a width of a main peak, carrying out correlation analysis on the extracted peak value position and width data of the main peak and the corresponding scatterer feature size based on a deep sub-wavelength scatterer sample with a known feature size, and constructing a peak value position mapping function and a peak width mapping function; and performing particle size evaluation based on the constructed function. According to the method, the problems of low signal-to-noise ratio, large out-of-focus interference, poor applicability and the like of a traditional method are solved, and the precision and the universality of particle size evaluation are remarkably improved.

The invention discloses a wafer detection scattered light collection objective lens, and relates to the technical field of optical imaging. The scattered light collecting lens is applied to wafer defect detection, the scattered light collecting lens sequentially comprises a first lens group, a second lens group, a third lens group and a fourth lens group from an object plane to an image plane in the optical axis direction of the lens, twelve lenses and an aperture diaphragm, and the twelve lenses and the aperture diaphragm are coaxially arranged; the first lens group is a catadioptric lens group, has positive focal power, and is used for imaging an object plane to an intermediate image plane through two times of reflection. The numerical aperture of the acquisition objective lens provided by the invention is equal to 0.9, the field of view is equal to 2.6 mm, the acquisition objective lens has the characteristics of large field of view, large numerical aperture and small center shielding, the capability of detecting scattered light of defects can be greatly improved, chromatic aberration is corrected by adopting a refraction and reflection mode, and a wider laser bandwidth can be realized compared with a general refraction mode of combining CaF2 and fused quartz.

The invention provides an optical imaging satellite task scheduling method based on a hybrid heuristic memetic evolution algorithm, solves the problem of how to accurately and efficiently obtain satellite images, belongs to the engineering field, and is based on a constructed satellite data set, a task data set, a satellite dynamics model, a task decision model and constraint conditions, takes maximizing the satellite task completion benefit value as an objective function, and constructs a satellite scheduling decision model; constructs a hybrid heuristic memetic evolution algorithm: obtains an initial observation path solution set through clustering processing and a constraint check model; inputs the initial solution into the satellite scheduling decision model to obtain a total benefit value of the initial solution; adopts a neighborhood search algorithm to obtain a feasible solution and a total benefit value of the initial solution, and obtains the quality of the feasible solution from the total benefit value difference; combines a simulated annealing algorithm and an ant colony evolution algorithm to search, and obtains an optimal solution for optical imaging satellite task scheduling after a preset stop condition is met; the invention realizes accurate and efficient acquisition of satellite images.

The invention provides an unmanned aerial vehicle full-band countering method and system based on acousto-optic-electric composite detection. According to the invention, through synchronous cooperative detection of sound wave sensing, optical imaging and electromagnetic spectrum analysis, a composite detection signal is generated; based on the relevance between the acoustic characteristic frequency band and the electromagnetic control frequency band, extracting the working mode characteristics of the unmanned aerial vehicle and generating dynamic interference parameters; generating a synchronous interference instruction according to the time domain modulation characteristic of the dynamic interference parameter; in combination with abnormal noise distribution characteristics captured by the acoustic module and flight path offset identified by the optical module, power distribution and frequency band coverage of the interference instruction are corrected in real time, and optimized interference parameters are generated; and finally, a dynamic interference map is generated through overlapping frequency spectrum fusion of the dynamic interference parameters and the optimized interference parameters, accurate cooperative suppression is realized, full-band real-time accurate suppression of navigation, communication and power control signals of the target unmanned aerial vehicle is realized, and the countering efficiency in a complex scene is remarkably improved.

The invention discloses a PCB detection method and device based on multi-modal fusion and an adaptive algorithm, and relates to the field of electronic manufacturing detection. The method comprises the following steps: acquiring optical imaging data by using an optical imaging unit, acquiring tomography data by using an X-ray unit, and acquiring three-dimensional point cloud data by using a laser unit; collecting a reference signal, and updating detection parameters of the optical imaging unit, the X-ray unit and the laser unit; performing feature level fusion to obtain multi-modal fusion data; and inputting the multi-modal fusion data into a detection model, and performing defect comparison in combination with a historical database to obtain a defect detection result of the PCB. According to the method, optical imaging, X-ray penetration and laser three-dimensional modeling technologies are combined, and full-dimensional defect detection of the surface, the interior and the three-dimensional structure of the PCB is covered, so that the defect accuracy is improved. Meanwhile, a traditional fixed parameter detection mode is broken through through a self-adaptive intelligent algorithm, dynamic parameter optimization and multi-scene self-adaption are achieved, and the detection efficiency is effectively improved.

An intelligent drone system for surveying and monitoring construction sites and industrial areas, consisting of: an AI processor for processing and analyzing data from multiple sensors, including LiDAR, thermal imaging, and high-resolution cameras, a laser-based distance measurement sensor for generating high-resolution 3D maps of the surveyed environment for topographic accuracy and real-time obstacle detection to improve navigation safety. a heat detection sensor for capturing infrared radiation to detect thermal anomalies that may indicate potential device or structural problems. an optical imaging system to provide detailed visual documentation of the surveyed construction site, where: The optical imaging system captures high-quality images to support inspections and monitoring, complements LiDAR and thermal data, provides a visual context for the captured data, and enables reporting for project tracking and compliance.

The invention provides an optical imaging system. The optical imaging system comprises a lens barrel, a lens group and a spacing element group, and the lens group and the spacing element group are arranged in the lens barrel. The six lenses are sequentially a first lens with negative focal power, a second lens with positive focal power, a third lens with positive focal power, a fourth lens with focal power, a fifth lens with focal power and a sixth lens with focal power from the object side to the image side; the spacing element group comprises a first spacing element, a second spacing element and a third spacing element; the following conditions are satisfied: 1lt; tr1r4 / Tr5r12lt, Tr1r4 / 1.2; the formula is as follows:-1.25 < = d2s / f12lt; and-0.5. According to the invention, the problem of discretization caused by increasing the thickness of the front-end lens in a six-piece optical imaging system in the prior art to satisfy a large field of view is solved.

The invention discloses an acoustic-optical imaging semantic fusion detection method for underwater defects of a hydraulic structure, and the method comprises the steps: obtaining underwater acoustic and optical images of the hydraulic structure at the same time through underwater acoustic-optical imaging sensing for the underwater defects of the hydraulic structure; and on the basis, acoustic semantic features in the acoustic image are extracted through a semantic feature extraction module, optical image features in the optical image are processed by taking the acoustic semantic features as guidance, and a defect detection result is obtained when an underwater defect target exists in the image, so that underwater defect sound-light imaging semantic fusion detection is realized. The technology is suitable for health monitoring of various dam system hydraulic structures, and operation safety and water safety of hydraulic engineering are guaranteed.

A system and method for determination of one or more dimensions (e.g., width or length) of an anatomic feature (e.g., bone) of a subject includes a radiographic imaging system configured to image the subject, an optical imaging system computer to determine the position of the subject relative to one or more components of the radiographic imaging system, and one or more processors configured to determine the one or more dimension of the anatomic feature based on an image, generated by the radiographic imaging system, of the anatomic feature and the determined position of the subject relative to one or more components of the radiographic imaging system.

The invention provides an evidence obtaining method and system based on image processing, and the method comprises the following steps: S1, generating a pixel-level depth-of-field distribution diagram of an input image through a multi-scale encoder-decoder network, employing an edge perception optimization layer in a decoding stage, and improving the depth-of-field boundary precision through minimizing a local gradient consistency loss function; s2, performing depth-of-field rationality verification based on an optical imaging physical model, and triggering a first-level tampering alarm by calculating a defocusing fuzzy radius and a gradient direction of a selected region when a difference between the defocusing gradient directions of a target region and a background region exceeds a preset threshold value; and S3, dynamically positioning a key pixel region, identifying a depth-of-field mutation boundary by using an edge detector, calculating by combining local texture complexity, screening a pixel set of which the entropy value is higher than a threshold value and which is located at the mutation boundary, correlating metadata to verify the rationality of the physical size and the spatial position of an object, and eliminating false detection caused by perspective transformation.

The invention relates to the technical field of smartphone surface defect detection, and discloses a smartphone defect detection method and system, and the method comprises the steps: firstly collecting defect detection image data through multi-dimensional optical imaging, and dividing the defect detection image data into a plurality of detection sub-regions; extracting surface defect features based on a texture enhancement technology, and extracting defect difference indexes in combination with pixel distribution and spatial correlation; then, the regularity of defect modes is analyzed and evaluated through multi-scale decomposition, the defect fluctuation amplitude is dynamically analyzed and evaluated through a sequential sequence, and the stability of defect difference indexes is judged by integrating the regularity and the fluctuation amplitude; and finally, comparing the stable defect difference index with reference data, and determining a detection result. According to the method, through multi-dimensional imaging, multi-scale analysis and dynamic evaluation, the recognition precision and reliability of complex surface defects are improved, the method is suitable for surface detection of smart phones of different materials and structures, and the high-quality detection requirement in large-scale production is met.

The invention provides an optical imaging lens, the optical imaging lens has eight lenses with focal power, and the optical imaging lens comprises a lens group, a spacing element group and a lens barrel group, the effective focal length f7 of the seventh lens and the interval EP67 between the sixth spacing element and the seventh spacing element in the direction of the optical axis of the optical imaging lens meet the following condition: f7 / EP67 is more than or equal to-8.25 and less than or equal to-6.82; the curvature radius R15 of the object side face of the eighth lens and the inner diameter d7m of the image side face of the seventh spacing element meet the condition that R15 / d7m is larger than or equal to 1.31 and smaller than or equal to 2.28. The multi-group optical imaging lens solves the problems that in the prior art, the rear end forming yield of the multi-group optical imaging lens is low, and stray light is serious.

The invention belongs to the technical field of physiological parameter measurement and testing, and discloses a multi-mode optical non-contact health monitoring system and method. The system comprises a sensing measurement module, a data synchronous acquisition module, a micro-motion stabilization and correction module, a three-dimensional trajectory reconstruction module, a multi-modal data processing module and a physiological index calculation module. Optical imaging data are processed preferentially through a U-Net network and then fused with video micro-motion features, and the problem of multi-modal data coupling interference in a traditional method is solved. A mathematical mapping relation between blood flow parameters and physiological indexes is established through a diffusion optical spectrum mapping network, the segmented blood flow parameters are mapped to initial physiological indexes, and the time lag problem of single-modal data is solved through time sequence features extracted through a time sequence feature fusion network. And the weights of the time sequence characteristics and the preliminary physiological indexes are dynamically adjusted through a space-time attention mechanism, so that the robustness in a complex environment is enhanced, and the prediction precision and stability of a non-contact health monitoring system are improved.

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 discloses a nondestructive testing device for a steel profile and a testing method of the nondestructive testing device. According to the method disclosed by the invention, the ultrasonic technology is combined with optical imaging, so that synchronous detection of surface and internal defects can be completed at one time. Ultrasonic waves penetrate through the interior of the steel profile, and hidden defects such as cracks and air holes are accurately recognized; the optical camera module captures visible problems such as surface scratches and corrosion in real time. The integrated detection mode avoids the tedious process of step-by-step operation required by a traditional method, for example, magnetic powder detection of surface defects and ultrasonic scanning of an internal structure are not needed, the detection period is greatly shortened, and the method is especially suitable for steel structure buildings, bridge engineering and other scenes with high requirements for detection efficiency and has good application prospects. The full-process automatic design effectively reduces the influence of human intervention, and ensures the scanning continuity of the detection area; the intelligent algorithm automatically analyzes the ultrasonic echo signal and the optical image data, and judges the defect level according to the preset standard, thereby avoiding subjective misjudgment of manual visual inspection.

The invention provides a flat-field apochromatic objective lens and an optical microscope, and relates to the technical field of optical imaging. The flat-field apochromatic objective lens comprises a first lens group, a second lens group and a third lens group which are sequentially arranged from an object plane to an image plane, both the first lens group and the second lens group have positive refractive power, the third lens group has positive refractive power or negative refractive power, the first lens group comprises at least two sub-lens groups with positive refractive power, and the second lens group comprises at least two sub-lens groups with negative refractive power. The sub-lens group is a single lens or a balsaming lens formed by gluing at least two single lenses, and the second lens group comprises at least one balsaming lens with positive refractive power and at least one balsaming lens with positive refractive power or negative refractive power. The third lens group includes at least one cemented lens having a positive refractive power and at least one cemented lens having a positive refractive power or a negative refractive power. On the basis, the flat-field apochromatic objective lens has a large field of view, a large numerical aperture and a long working distance.

The invention discloses an ultrasonic enhanced tactile sensor system, a sensor and a sensing method. The tactile sensor system comprises a multi-mode sensing unit and a processing unit. The multi-mode sensing unit integrates an ultrasonic module and a visual tactile sensing module, ultrasonic acoustic impedance matching and optical imaging transmission are achieved through an acousto-optic matching structure, and ultrasonic echo signals and tactile deformation images can be synchronously collected. In the processing unit, an event triggering module detects a contact event based on a tactile image and generates a switching signal, and a self-adaptive processing module dynamically controls an ultrasonic mode, distance measurement during non-contact, and switching to object internal imaging or material property identification during contact according to the switching signal. And the three-level fusion processing module outputs a multi-modal sensing result through signal-level synchronous acquisition, feature-level cross-modal fusion and decision-level task switching. According to the system, multi-dimensional perception of proximity sense, surface texture and deep information is realized, and the pre-judgment and accurate operation capability of the robot in a complex scene is improved.

The invention provides wafer detection equipment and a wafer detection method, which are used for solving the problem of how to improve the wafer detection efficiency. The wafer detection equipment comprises a double-sided detection unit, the double-sided detection unit comprises a scanning carrying platform, a front optical imaging module and a back optical imaging module, and the scanning carrying platform can linearly move along a first direction; the front optical imaging module and the back optical imaging module are respectively arranged on two opposite sides of the scanning stage and are respectively provided with a back bright field light source and a back dark field light source; when the scanning stage moves along the positive direction of the first direction, the front bright field light source and the back bright field light source are synchronously turned on, and the front optical imaging module and the back optical imaging module respectively generate bright field images of the front and back of the wafer; and when the scanning stage moves along the negative direction of the first direction, the front and back dark field light sources are synchronously turned on, and the front optical imaging module and the back optical imaging module respectively generate dark field images of the front and back of the wafer.

The invention relates to the technical field of precise positioning, in particular to a corneal topographic map-based full-automatic fixed-focus positioning system, which comprises an optical imaging module for acquiring multi-angle optical signals on the surface of a cornea through an optical device, processing light reflection in different areas, determining the edge of the cornea by using the intensity difference of the reflected light, and calculating the reflection angle and intensity; and identifying the light signal of the key area to obtain light reflection characteristic data. According to the method, accurate analysis of the complex form of the cornea surface is achieved through accurate analysis of the light reflection characteristic data, the accuracy of focal length control is improved, a PID control algorithm is applied, the curvature change data is combined, the high matching degree of focal length adjustment and the cornea surface curvature is ensured, the stability and response speed of focal length adjustment are improved, and the accuracy of focal length adjustment is improved. Through light path deviation correction, imaging errors caused by light path deviation are reduced, and the accuracy, the stability and the imaging quality of automatic focus fixing and positioning of the corneal topography are improved.

The invention provides an optical imaging device, which comprises a lens group, a spacing element group and a lens barrel, the lens group comprises a first lens, a second lens, a third lens, a fourth lens, a fifth lens, a sixth lens and a sixth lens which are sequentially arranged at intervals from the object side to the image side of the optical imaging device, and the half Semi-FOV of the maximum half field angle of the optical imaging device and the numerical aperture fno of the optical imaging device meet the following conditions: 1lt; fno / tan (Semi-FOV) < = 1.25; the effective focal length f of the optical imaging device and the effective focal length f1 of the first lens meet the following formula:-2.3 lt; f1 / flt; -1.2,-1.2; the inner diameter d0s of the object side end face of the lens barrel, the inner diameter d1s of the object side face of the first spacing element and the effective focal length f1 of the first lens meet the following condition:-1.45 < = (d0s-d1s) / f1lt; and-0.85. The problem that in the prior art, an ultra-wide-angle and large-aperture optical imaging device is serious in stray light is solved.

The invention relates to the technical field of chip optical detection, and discloses a high-precision defect detection method and system for a semiconductor chip. Initial detection data are obtained through laser scattering and photo-thermal microscopic detection, key visual features are extracted and converted into a multi-dimensional visual feature matrix, and the multi-dimensional visual feature matrix is used for detecting the defects of the semiconductor chip; according to the method, a fuzzy sensing area is positioned and potential defects are identified by using a map clustering algorithm, adaptive analysis of a supplementary visual angle is carried out and supplementary detection data is generated, and finally defect identification is carried out through the supplementary data and popularity information to obtain a chip defect result. The method effectively improves the precision and reliability of defect detection, especially has obvious advantages in recognition of complex and fuzzy regions, and solves the problem of insufficient precision of single-path optical imaging detection in the prior art.

The invention provides an optical imaging lens. The optical imaging lens comprises a lens barrel, a lens group and a spacing element group, and the lens group and the spacing element group are arranged in the lens barrel. The six lenses are sequentially a first lens with negative focal power, a second lens with positive focal power, a third lens with positive focal power, a fourth lens with negative focal power, a fifth lens with positive focal power and a sixth lens with negative focal power from the object side to the image side along the optical axis of the optical imaging lens; the following conditions are satisfied: 1.00 lt; cT5 / EP45 / N5lt; 1.75, 1.75; f4 / (EP34 + CP4) is greater than or equal to-2.05 and less than or equal to-1.35; and (D5m-d4s) / CP5 is more than or equal to 3.59 and less than or equal to 5.60. According to the invention, the problem that the size of the lens is increased due to the fact that a six-piece optical imaging lens in the prior art meets the performance requirement, and thus discretization is caused is solved.

The invention provides an optical imaging lens. The optical imaging lens comprises a lens barrel, a lens group and a spacing assembly, wherein the lens group and the spacing assembly are accommodated in the lens barrel; the lens group comprises a first lens with negative focal power, a second lens with positive focal power, a third lens with negative focal power, a fourth lens with positive focal power and a fifth lens with negative focal power which are sequentially arranged from the object side to the image side along the optical axis. The sixth lens has positive focal power; the seventh lens has negative focal power; the spacing assembly comprises a second spacing element which is arranged between the second lens and the third lens and is in contact with the image side surface of the second lens, and a third spacing element which is arranged on the image side of the third lens and is in contact with the image side surface of the third lens; the optical imaging lens satisfies the following conditions: 7.69 mm < = f * tan (HFOV) < = 9.27 mm, and 33.65 < EP23 / T23 < 59.35.

The invention discloses an intelligent tobacco impurity removal method, a storage medium and a system. A material monolayer processing device, an optical imaging unit, a detection processing unit, a time sequence and rejection control unit, an electrical control unit and a blowing electromagnetic valve are adopted to form the tobacco impurity removal method based on the intelligent tobacco impurity removal system of an image. According to the invention, an image recognition technology combining a color search algorithm and an AI intelligent algorithm is used, the types, distribution conditions and the like of sundries in the material can be counted, and according to different weights, forms and motion tracks of the sundries, the track change and floating of the sundries in the flying process are reduced through the algorithm, so that the accuracy of the sundries is improved. Richer and more accurate sundry movement data are provided for execution of subsequent sundry removal actions, and data support is provided for quality control analysis; and sundries in the fragments can be accurately removed, so that the purity of the tobacco leaves is improved.

The invention provides an efficient crack detection and intelligent risk assessment system, belongs to the technical field of structural health monitoring, and is particularly suitable for crack detection in buildings, bridges, tunnels and industrial equipment. According to the system, comprehensive detection on the surface and the interior of a crack is realized by combining an optical imaging sensor, a laser scanning sensor and an ultrasonic sensor through a multi-mode sensor fusion technology. The system comprises a data acquisition unit, a data processing unit, a multi-modal data fusion module, a three-dimensional modeling module and an intelligent decision support module. Through an adaptive learning algorithm and a deep learning technology, the system can dynamically optimize a detection model and provide a high-precision detection result. Meanwhile, the system supports three-dimensional reconstruction and visual display of the crack, carries out real-time assessment on the crack risk through an intelligent decision support module, automatically generates a health assessment report and gives out an early warning. Compared with traditional manual detection, the system has the advantages that the comprehensiveness, the automation degree and the efficiency of detection are greatly improved, and the system is suitable for structural health monitoring in various complex scenes.

The invention discloses a snapshot type hyperspectral imaging industrial online sorting system which comprises a support assembly, an optical imaging assembly and a motion control assembly. The bracket assembly consists of a bottom plate, and an adjustable bracket and a fixed bracket which are fixed on the bottom plate; the optical imaging assembly comprises a lighting unit, an imaging objective lens and a snapshot type hyperspectral camera, and can synchronously obtain space image information of a measured object and spectral information of a continuous wave band in a single exposure process; the motion control assembly realizes automatic transmission of the measured object through the transmission platform; the data processing module is integrated with an image preprocessing unit, a spectral analysis unit and a spectral database, and is used for end-to-end closed-loop control from original hyperspectral data generation to sorting decision making; the system adopts a metasurface light splitting structure to realize real-time decoding, performs material or category identification by using a pre-trained classification model, and finally automatically generates a sorting decision signal according to an identification result to drive a motion control assembly to complete sorting operation.