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CN-121994801-A - Multi-mode visual imaging system and method based on intelligent sensor

CN121994801ACN 121994801 ACN121994801 ACN 121994801ACN-121994801-A

Abstract

The application provides a multi-mode visual imaging system and method based on an intelligent sensor, which are applied to the technical field of data processing. The multi-mode imaging system based on the liquid zoom self-adaptive adjustment and multi-light source time sequence switching comprises the steps of collecting core data such as imaging module parameters and light source characteristics, performing standardized processing to generate system basic data, determining 2D-3D-multi-spectrum association thresholds and light source switching coefficients based on collaborative constraint design modeling specifications, constructing multi-level collaborative mechanisms such as liquid zoom self-adaptive adjustment and multi-light source time sequence switching, integrating the data, the specifications and the mechanisms, optimizing through a special algorithm, and conforming to a complete process of power-on-calibration-detection-analysis-reporting, so that multi-mode imaging with module collaborative characteristics, multi-spectrum information, dynamic focusing properties and high-precision 3D reconstruction is achieved, and the problems of low integration, poor adaptation and the like of a traditional system are effectively solved.

Inventors

  • LI RUI

Assignees

  • 苏州锐新视科技有限公司

Dates

Publication Date
20260508
Application Date
20260130

Claims (8)

  1. 1. A multi-modal visual imaging method based on intelligent sensors, comprising: Based on multi-mode imaging requirements and a composite system functional target, carrying out structured acquisition and standardization processing on imaging module parameters, light source wavelength characteristics, structured light projection angles and time sequence control logic to generate system basic data comprising functional module types, light path correlation intensity and imaging time sequence parameters; Designing a modeling rule of system data based on multimode collaborative imaging constraint characteristics, and defining an imaging correlation threshold value and a light source switching response coefficient of 2D-3D-multispectral to generate a multimode imaging modeling specification; setting a multi-level cooperative mechanism of liquid zoom lens self-adaptive adjustment, multi-light source time sequence switching, precise scanning of a region of interest and modularized detachable adaptation by combining the centralized control of a control unit and the multi-module cooperative requirements; And (3) integrating and executing system basic data, a multi-mode imaging modeling specification and a multi-level cooperative mechanism, iteratively optimizing an imaging effect through a target image processing algorithm, and generating a multi-mode imaging result comprising module cooperative characteristics, multi-spectrum semantic information, dynamic focusing time sequence attributes and 3D morphology reconstruction precision according to a complete workflow of system power-on, calibration, parameter setting, 2D appearance detection, 3D scanning suspected defect areas, data processing analysis, generation of detection reports and system outage.
  2. 2. The method of claim 1, wherein the structured acquisition and normalization of imaging module parameters, light source wavelength characteristics, structured light projection angles, timing control logic based on multi-modality imaging requirements and composite system functional targets generates system base data comprising functional module type, light path correlation intensity, imaging timing parameters, comprising: Based on multi-mode imaging requirements, composite system integration targets and industrial detection precision requirements, integrating imaging module core parameters, light source characteristic parameters, structured light projection key parameters and time sequence control logic information, and determining acquisition ranges and association requirements of all module parameters; designing parameter acquisition logic based on data standardization processing requirements, and determining acquisition standards of core parameters and auxiliary parameters, wherein the core parameters comprise camera resolution, working distance of a liquid zoom lens and light source wavelength range, and the auxiliary parameters comprise module installation angle and connection interface type information; Setting parameter optimization rules for independently controlling the LED lamp sets of the multispectral annular light source and accurately adjusting the voltage signals of the liquid zoom lens by combining the imaging stability requirement of the complex texture surface, and ensuring the data effectiveness; And processing the parameter acquisition requirement, the data standardization processing requirement and the parameter optimization rule to generate system basic data comprising parameter types, acquisition specifications, association logic and optimization strategies.
  3. 3. The method of claim 1, wherein designing modeling rules for system data based on multi-modal collaborative imaging constraint characteristics, defining imaging correlation thresholds for 2D-3D-multispectral, light source switching response coefficients, and generating multi-modal imaging modeling specifications comprises: based on multimode collaborative imaging constraint characteristics, inter-module light path adaptation requirements and imaging mode switching efficiency targets, classifying and integrating association logic, light source switching time sequence and parameter adaptation standards of the 2D-3D-multispectral, and generating modeling basic information comprising association types, switching logic and adaptation ranges; planning modeling rule design logic based on imaging precision and flow smoothness requirements, and defining setting standards of imaging association thresholds and light source switching response coefficients to generate rule design core parameter information; setting an optimization rule for dynamically adjusting an association threshold value and differentially configuring a response coefficient according to an imaging mode by combining imaging stability of a complex texture surface and different depth of field adaptation requirements, and ensuring the adaptation of multi-mode cooperation; and processing modeling basic information, rule design core parameters and optimization rules to generate a multi-mode imaging modeling specification comprising association constraint standards, time sequence control specifications, parameter adaptation requirements and optimization strategies.
  4. 4. The method of claim 1, wherein setting a multi-level collaboration mechanism for liquid zoom lens adaptive adjustment, multi-light source timing switching, precise scanning of a region of interest, and modular detachable adaptation in combination with control unit centralized control and multi-module collaboration requirements comprises: Based on the centralized control requirement of the control unit and the multi-module cooperative target, working parameters and interaction logic of the imaging module, the light source and the structured light projection module are obtained, and the adjustment characteristics, the light source switching time sequence, the scanning area positioning precision and the modularized adaptation requirement of the liquid zoom lens are extracted; carrying out compliance verification on the extracted module parameters and interaction logic, and confirming parameter suitability, time sequence coordination, positioning accuracy and adaptation compatibility to generate a module collaborative verification result; Combining different depth of field adaptations and complex texture imaging requirements, planning an implementation scene of a multistage cooperative mechanism, and defining starting conditions and execution standards of each mechanism according to application scenes of 2D imaging, multispectral imaging and 3D scanning to generate scene planning parameters; Setting a cooperative fault tolerance rule based on the actual requirements of industrial detection efficiency and precision, and defining tolerable adjustment response delay, switching interval deviation, scanning positioning error and adaptive reset error range; Integrating scene planning parameters and fault tolerance rules, formulating a multi-level collaborative mechanism operation specification, and defining a triggering flow, a parameter adjustment mode and module linkage logic of each mechanism; The integration module cooperates with the check result, the scene planning parameter, the fault-tolerant rule and the operation specification to generate multi-level cooperation mechanism implementation result information comprising cooperation mechanism definition, trigger conditions, execution standards, fault-tolerant range and operation flow.
  5. 5. The method of claim 4, wherein the system base data, the multi-modal imaging modeling specification and the multi-level collaboration mechanism are integrally executed, the imaging effect is iteratively optimized by a target image processing algorithm, a complete workflow comprising module collaboration features, multi-spectral semantic information, dynamic focusing time sequence attributes and 3D morphology reconstruction accuracy is generated following a system power-on-calibration-parameter setting-2D appearance detection-3D scanning suspected defect area-data processing analysis-generation detection report-system power-off, comprising: according to the integration requirement of multi-mode imaging and the industrial detection precision target, integrating and calling system basic data, multi-mode imaging modeling specifications and a multi-level collaboration mechanism, extracting core information such as module collaboration parameters, imaging constraint standards, mechanism execution logic and algorithm optimization requirements and the like; based on a complete workflow design step-by-step execution scheme, determining operation standards of all links of system power-on initialization, calibration, parameter setting, 2D appearance detection, 3D scanning suspected defect areas, data processing analysis, detection report generation and system power-off, and generating a preliminary result of 2D image, multispectral data and 3D morphology reconstruction by iterative optimization of imaging effects through a special image processing algorithm; According to imaging precision requirements and data validity standard design result optimization schemes, standard reaching threshold values and defect region positioning error ranges of 3D morphology reconstruction precision are defined, 3D coordinate calculation results are corrected by combining a triangulation principle, 2D image defect recognition results are subjected to cross verification, and optimized multidimensional imaging data are generated; Combining the multi-mode information fusion requirement with the detection report specification, setting a result integration check rule, and generating a result check report by confirming the consistency of the cooperative characteristics of the modules, the accuracy of multi-spectrum semantic information, the rationality of dynamic focusing time sequence and the standard reaching of 3D precision; and integrating the step-by-step execution scheme, the result optimization scheme, the verification rule and the verification report to generate multi-mode imaging final result information comprising module cooperative characteristics, multi-spectrum semantic information, dynamic focusing time sequence attributes, 3D morphology reconstruction precision and defect detection details.
  6. 6. A multi-modality visual imaging system based on intelligent sensors, the system comprising: The system basic data acquisition and standardization module is used for acquiring core data comprising imaging module parameters, light source wavelength characteristics, structured light projection angles and time sequence control logic, and generating system basic data comprising function module types, light path association strength and imaging time sequence parameters through structured acquisition and standardization processing; The multi-mode imaging modeling specification design module is used for designing a system data modeling rule based on multi-mode collaborative imaging constraint characteristics, determining an imaging correlation threshold value and a light source switching response coefficient of 2D-3D-multispectral, and generating a multi-mode imaging modeling specification; The multi-level cooperative mechanism setting module is used for combining the centralized control of the control unit and the multi-module cooperative requirements to set a multi-level cooperative mechanism of the liquid zoom lens, such as self-adaptive adjustment, multi-light source time sequence switching, precise scanning of the region of interest and modularized detachable adaptation; The multi-mode imaging integration execution and result generation module is used for integrating system basic data, multi-mode imaging modeling specifications and a multi-level cooperative mechanism, iteratively optimizing an imaging effect through a target image processing algorithm, and generating a multi-mode imaging result comprising module cooperative characteristics, multi-spectrum semantic information, dynamic focusing time sequence attributes and 3D morphology reconstruction precision according to a complete workflow of system power-on, calibration-parameter setting-2D appearance detection-3D scanning suspected defect area-data processing analysis-generation detection report-system power-off.
  7. 7. An electronic device, comprising: And a memory for storing executable instructions of the first processor; Wherein the first processor is configured to perform the multi-modal visual imaging method in accordance with any one of claims 1-5 via execution of the executable instructions.
  8. 8. A computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a second processor implements the multimodal visual imaging method based on intelligent sensors as claimed in any of claims 1 to 5.

Description

Multi-mode visual imaging system and method based on intelligent sensor Technical Field The invention relates to the technical field of data processing, in particular to a multi-mode visual imaging system and method based on an intelligent sensor. Background In the technical field of machine vision detection and three-dimensional measurement, the multi-mode vision imaging system is widely applied to scenes such as industrial detection due to the fact that the multi-mode vision imaging system can integrate functions such as 2D imaging, multispectral imaging and 3D scanning. However, the prior art still has a plurality of defects, and is difficult to meet the detection requirements of high precision and high efficiency, and the specific problems are as follows: And meanwhile, the modularized design is insufficient, the flexibility of function configuration is poor, a user is difficult to flexibly increase and decrease the modules according to actual detection requirements, and the initial input cost and the use complexity are increased. The traditional system adopts fixed focal length lenses, cannot adapt to the detection requirements of workpieces with different heights, needs to manually replace the lenses when facing different depth-of-field scenes, is complex in operation and easy to influence detection continuity, and has the problems of low mechanical focusing speed and low precision even if part of the system has a focusing function, so that the dynamic detection scene is difficult to adapt quickly. Existing systems often employ a single light source, which is difficult to adapt to imaging requirements of object surfaces of different colors and textures. In complex texture surface detection, problems such as reflection interference, texture detail loss or defect identification blurring are easy to occur, imaging quality is unstable, accuracy of detection results is affected, and the synergy of multispectral imaging and 2D and 3D imaging is poor, so that complementary advantages of the multimodal data cannot be fully exerted. The prior art lacks clear multi-mode cooperative constraint rules and modeling specifications, has unclear association logic and threshold standard between 2D-3D-multispectral imaging, has poor timing coordination of light source switching and mode switching, is easy to generate illumination superposition interference or switching delay, causes unsmooth imaging flow, and simultaneously lacks a dynamic optimization mechanism aiming at different scenes, and is difficult to adapt to diversified requirements of complex industrial detection environments. It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art. Disclosure of Invention Other features and advantages of the application will be apparent from the following detailed description, or may be learned by the practice of the application. According to one aspect of the application, a multi-mode visual imaging method based on an intelligent sensor is provided, which comprises the steps of carrying out structured acquisition and standardization processing on imaging module parameters, light source wavelength characteristics, structured light projection angles and time sequence control logic based on multi-mode imaging requirements and a composite system function target to generate system basic data comprising function module types, light path correlation intensities and imaging time sequence parameters; the method comprises the steps of designing modeling rules of system data based on multimode collaborative imaging constraint characteristics, defining imaging associated thresholds and light source switching response coefficients of 2D-3D-multispectral to generate multimode imaging modeling specifications, setting a multistage collaborative mechanism of liquid zoom lens self-adaptive adjustment, multi-light source time sequence switching, region-of-interest accurate scanning and modularized detachable adaptation by combining control unit centralized management and multi-module collaborative requirements, carrying out integrated execution on system basic data, multimode imaging modeling specifications and multistage collaborative mechanism, carrying out iterative optimization of imaging effects through a target image processing algorithm, and generating multimode imaging results comprising module collaborative characteristics, multispectral semantic information, dynamic focusing time sequence attributes and 3D morphology reconstruction accuracy by following a complete workflow of system power-on-calibration-parameter setting-2D appearance detection-3D scanning suspected defect region-data processing analysis-generation detection report-system power-off. The application also discloses a multi-mode visual imaging system base