48638 results about "Light source" patented technology

The invention discloses a welded pipe surface defect detection system, particularly relates to the technical field of metal pipe surface quality detection, and is used for solving the problem of defect misjudgment and missing detection caused by mixed reflection interference under single light source irradiation in the existing visual detection technology. An annular light source is adopted for time-sharing pulse triggering through a light source control module, reflected light images at different angles are synchronously collected, a feature extraction module constructs a diffuse reflection intensity ratio and mirror reflection angle distribution matrix to generate a three-dimensional reflection feature spectrum, and reflection anomaly characterization of a defect area is enhanced; the area positioning module screens candidate areas and inhibits interference by combining strength ratio circumferential deviation degree and reflection angle gradient change, and the distortion correction module compensates geometric distortion errors based on curvature radius and station movement parameters, filters pseudo defects of abnormal time sequence fluctuation, and improves the accuracy of the distortion correction. And the defect judgment module utilizes a gradient-gray scale space coupling classification model to distinguish the types of cracks, scratches and corrosion, and finally judges the defect authenticity in combination with reflection characteristic deviation vector superposition.

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

The disclosure discloses a surface defect detection method, system, equipment, and terminal, comprising: S1, LBP operator: before comparing the benchmark image with the actual shot image, a LBP texture feature extraction algorithm is first used for pre-processing; S2, Sift feature-point matching: adding a Sift feature-point matching algorithm to calculate key feature points in the image and compare; S3, defect detection: when the local feature points of the shot image are successfully matching with a certain image in the benchmark image library, a detailed comparison will be made and complete defect detection. The disclosure integrates various image processing methods in advance without any setting by users. After running the system with one click, it can automatically perform detection, greatly reducing personnel training costs. The disclosure has simple structure and low cost. The basic structure only needs a computer, camera, and light source, making it easy to use and with highly mobile.

The invention belongs to the technical field of welding quality detection, and particularly relates to a visual image-based welding seam defect detection method, which comprises the steps of image acquisition, image preprocessing, data analysis, data output, defect classification and decision making, system closed-loop optimization and the like. According to the method, by synchronously collecting two-dimensional images, three-dimensional shapes and heat distribution data of metal welding seams and adopting a polarization filter and annular LED light source combination scheme, multi-dimensional conjoint analysis of physical defects and thermodynamic characteristics is achieved, basic characteristic data are extracted through primary processing, quantifiable defect coefficient indexes are generated through secondary processing, and the detection accuracy is improved. And finally, generating a comprehensive defect index through a multi-modal fusion algorithm, constructing a well-arranged intelligent analysis decision chain, and establishing a self-evolution mechanism of data acquisition-analysis decision-model iteration through real-time interaction of a detection result and an algorithm model. The system can continuously optimize the detection threshold value and the characteristic weight parameter according to the actual working condition of the production line, and the continuous improvement of the detection sensitivity is kept.

The invention discloses a manipulator grabbing method based on deep learning target detection and image segmentation, and relates to the technical field of artificial intelligence and robotics.The manipulator grabbing method comprises the following steps that a scene image to be processed is collected, the image quality is improved through the multi-light-source fusion image enhancement technology, and recognition errors caused by uneven illumination are reduced; and inputting the enhanced image to a pre-trained deep learning model, executing a target detection task, and outputting an initial bounding box and a category label of the target object. According to the method, through multi-light-source image enhancement and high-precision image segmentation, the accuracy of target recognition and contour extraction is remarkably improved, and the capture failure rate caused by image misjudgment is reduced. And meanwhile, geometric consistency verification and a multi-factor grabbing scoring mechanism are introduced, dynamic screening and collision pre-detection are conducted on the paths, the grabbing stability and safety of the mechanical arm in the complex environment are effectively guaranteed, and the intelligence and robustness of the whole system are remarkably improved.

The invention relates to an injection molding product defect detection method based on machine vision, which comprises the following steps: collecting material information of a to-be-detected injection molding product in real time, and dynamically matching and adjusting light source parameters according to spectral reflection characteristics of materials to ensure image collection quality; secondly, the collected images are preprocessed, edge features and texture features are extracted, a three-dimensional model is constructed through multi-view image splicing, and three-dimensional defect features are extracted; thirdly, the multi-dimensional features are input into a deep learning model, the defect probability is calculated through feature fusion and forward propagation, and whether the product has defects or not is judged; if the defect exists, further identifying the defect category, and calculating the number and size of the defect; and generating a standardized detection report based on the defect information. According to the method, the image adaptability of products made of different materials is improved through dynamic light source adjustment, the two-dimensional and three-dimensional features are fused, the defect recognition accuracy is improved, and full-process automation from qualitative judgment to quantitative analysis of the defects is achieved.

The invention discloses an intelligent quality detection system based on machine vision, relates to the technical field of data processing, and solves the problem that light source parameters are difficult to automatically adjust by combining and utilizing a control algorithm according to environmental parameter influence coefficients. After multi-scale fusion alignment is difficult to carry out on image data, optimized image information is obtained by adopting a Q-learning algorithm and combining image quality scores; defect identification detection and three-dimensional deformation detection are difficult to carry out; and the quality abnormal coefficient is difficult to analyze and control feedback and visualization are difficult to carry out. Through adaptive light source control and image optimization, in combination with defect detection and three-dimensional deformation evaluation, efficient and accurate quality detection is realized, and through real-time feedback and visualization, the defect and deformation state of an object can be conveniently and rapidly known.

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

The invention relates to the technical field of semiconductors, and particularly discloses a multiband LED chip and a preparation method thereof, and a white light LED light source, the LED chip comprises a light-emitting layer, and the light-emitting layer comprises a first cyan light multi-quantum well layer, a second long-wave blue light multi-quantum well layer, a third short-wave blue light multi-quantum well layer and a fourth purple light multi-quantum well layer which are sequentially stacked in the epitaxial direction; the first cyan light multi-quantum well layer, the second long-wave blue light multi-quantum well layer, the third short-wave blue light multi-quantum well layer and the fourth purple light multi-quantum well layer are each of a periodic structure formed by alternately stacking well layers and barrier layers, and the light-emitting wavelengths of the first cyan light multi-quantum well layer, the second long-wave blue light multi-quantum well layer, the third short-wave blue light multi-quantum well layer and the fourth purple light multi-quantum well layer are sequentially decreased. And the forbidden band widths of the barrier layers are sequentially decreased. According to the LED chip, the overall excitation efficiency can be effectively improved, and the output full-spectrum white light LED light source is higher in color rendering index, better in stability, less in damage to human eyes and high in light emitting efficiency.

The invention relates to the technical field of cable insulation layer detection, in particular to a cable insulation layer detection device and method. Comprising a visual detection mechanism, a collision detection mechanism, a marking mechanism and a transmission mechanism, the visual detection mechanism comprises a first support frame, three detection cameras, an annular light source, three first mounting plates, a light shield and a light shielding cylinder, and the first mounting plates and the detection cameras are annularly distributed around the axis of a cable insulation layer; the abutting detection mechanism comprises a second supporting frame, a rotating disc, an abutting detection assembly and a position adjusting assembly. The marking mechanism comprises a coating pipeline, a brush and a switching assembly. The device is suitable for detection of cable insulation layers of different diameter models, through cooperation of visual detection with the shading device and the annular light source, images of the cable insulation layers are captured in multiple directions, and various appearance defects are efficiently detected; the collision detection can be accurately adjusted according to the diameter of the cable, the visual detection is assisted to improve the detection comprehensiveness and accuracy, and the defect position can be marked.

The invention relates to the technical field of industrial automatic detection and intelligent operation and maintenance, in particular to a machine vision and big data fused linear slide rail defect identification and prediction system, which comprises a machine vision acquisition module, a time sequence feature extraction module, a big data analysis platform, a defect prediction and decision module and the like. The machine vision acquisition module acquires a slide rail image through an annular camera array and a multi-angle light source, and the acquisition quality is ensured through motion compensation; the time sequence feature extraction module utilizes a space-time attention model to mine defect features; the big data analysis platform is associated with the multi-source data to construct a defect evolution graph; the defect prediction and decision-making module outputs residual life prediction and maintenance instructions based on double-target reinforcement learning, and the dynamic model updating unit optimizes the model according to errors. According to the method, high-precision identification, prospective prediction and intelligent decision making of the defects of the linear sliding rail are realized, the equipment reliability can be effectively improved, the operation and maintenance cost is reduced, and the method is suitable for a complex industrial environment and has wide application value.

The invention provides a photoelectric product detection method and system based on image processing, and relates to the technical field of image recognition, a complex defect detection sample is obtained by performing adaptive dynamic scaling on a multi-modal image on the surface of a photoelectric product, and a multi-scale feature vector is extracted from the complex defect detection sample; a fusion scale feature map of the photoelectric product is constructed based on the multi-scale feature vector and a light source mode, and a defect area is obtained through preliminary detection in the fusion scale feature map; constructing a defect subdivision model according to the feature entropy and the local gradient information of the defect region, and performing secondary segmentation and defect type discrimination on the defect region through the defect subdivision model to obtain a fine segmentation result; and pixel-level intensity adjustment is carried out on the defect area based on the defect probability graph of the defect area and the fine segmentation result to obtain a defect detection result of the photoelectric product, and optimal detection of fine defects on the surface of the photoelectric product can be realized so as to improve the reliability of the detection result of the photoelectric product.

The invention provides an intelligent real-time detection method and system for PCCP welding quality, and relates to the technical field of online detection and intelligent evaluation of pipeline welding quality through machine learning. Light energy data and multi-light-source images of a spiral weld pool are collected, exposure parameters are dynamically adjusted through the energy difference of visible light near-infrared bands, and the real-time detection of the PCCP welding quality is achieved. Inhibiting strong light interference and generating a weld surface image; a stress concentration area is positioned by scanning a welding seam thermal deformation area and combining speckle pattern change, sound frequency change and the elastic characteristic of the thin-wall steel cylinder; inputting the surface image and the deformation data into a space-time convolutional neural network, fusing light energy change, image details and spatial features to construct a weld joint space structure diagram, and adaptively correcting the position of a sensor; and comparing the sinking depth of the three-dimensional point cloud reconstruction, analyzing the correlation between the sinking degree and the stress, and generating a probability thermodynamic diagram to output the pressure-bearing failure risk level, so that the probabilistic early warning of the pressure-bearing failure risk can be realized.

The invention discloses an egg surface microcrack detection system and method based on image analysis, and relates to the field of surface defect nondestructive detection.The method comprises the steps that a process identification interface module obtains a processing process identifier of an egg in real time; the multi-angle annular light source module is provided with a multi-band annular light source array comprising visible light and near-infrared LED lamp beads which are independently controlled, a polaroid group and a beam splitter prism are integrated, and visible light polarization images and near-infrared polarization images on the surfaces of the eggs are synchronously collected; the thermal excitation enhancement unit obtains a thermal infrared image; the process filtering module extracts the axial elongation of the mechanical stress microcrack after graded transportation, and calculates the mesh fractal dimension of the thermal stress microcrack after UV disinfection; the multi-modal feature fusion module generates a dual-channel crack probability graph; and the dynamic feedback control module outputs a micro-crack risk grade and adjusts the rotating speed of the objective table and the light source intensity. The method has the advantages that accurate judgment of mechanical and thermal stress cracks is realized, and curved surface reflection interference is broken through.

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

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

The invention relates to the technical field of three-dimensional imaging, and discloses a high-precision underwater structured light three-dimensional point cloud imaging method and system, and the method comprises the following steps: deploying a plurality of sensor nodes, monitoring the temperature, salinity and turbidity key parameters of a water body in real time, selecting proper light source wavelength and power according to the collected data, and carrying out the three-dimensional point cloud imaging of the underwater structured light. It is ensured that the optimal signal-to-noise ratio is obtained under the current condition, and meanwhile the exposure time and gain setting of the camera are initialized. According to the method, the optimal signal-to-noise ratio is obtained by monitoring water body conditions in real time and dynamically adjusting light source characteristics, the optimal structured light pattern is predicted by using a machine learning model, scattering noise is removed, advanced registration and fusion algorithms and an accurate phase unwrapping method are applied, and a high-quality comprehensive structured light pattern is generated. Point cloud gaps are filled through a geometric correction algorithm and an interpolation / filtering algorithm, so that a reconstruction model is closer to the surface of a real object.

The invention relates to the technical field of water quality detection, and discloses a water quality pollutant detection method, system and equipment based on spectral analysis, and the method comprises the following steps: carrying out real-time spectral data acquisition on a water body sample in a vehicle driving process through a multispectral sensor array in a vehicle-mounted water quality detection device; obtaining a water body spectrum three-dimensional data cube; carrying out anti-vibration wavelength calibration and ambient light source interference elimination processing to obtain a preprocessed spectrum data set; performing standard multivariate decomposition processing and horizontal local constraint optimization of a pollutant target wave band on the preprocessed spectrum data set to obtain enhanced feature fingerprint spectrums for different types of water quality pollutants; and inputting the enhanced feature fingerprint spectrum into a deep belief network model to perform pollutant feature matching analysis to obtain various pollutant types and corresponding concentration values in the water body sample, thereby effectively eliminating ambient light changes and other background interferences, not only determining the pollutant types, but also accurately quantifying the concentration values.

The invention provides a highway tunnel intelligent illumination control method and system, and the method comprises the steps: collecting a real-time perception data set of a plurality of monitoring nodes in a target tunnel, covering a light source operation parameter, an illumination intensity parameter and an environment state parameter sequence, carrying out the feature extraction of the real-time perception data set, and carrying out the feature extraction of the real-time perception data set; generating a global feature set including light source operation stability, regional illumination coordination and environmental coupling influence features, and then inputting the global feature set into a preset dynamic decision model for strategy decision to obtain a multi-stage illumination regulation and control strategy set covering different regions, including target brightness, dimming time sequence and equipment cooperation parameters; and finally, generating an executable instruction set according to the multi-stage illumination regulation strategy set, issuing the executable instruction set to each partition illumination control terminal, and triggering adaptive dimming of the illumination equipment, thereby realizing intelligent management and control of tunnel illumination, improving an energy-saving effect and illumination quality, and ensuring driving safety.

The invention belongs to the technical field of computer vision, and discloses a wire harness crimping defect detection system and method based on machine vision, and the system comprises a multispectral light source control module which is used for employing an annular LED light source and coaxial light mixed illumination and adapting to different wire harness surface materials by dynamically adjusting the angle and intensity of the light source; the multi-dimensional perception acquisition module is used for acquiring wire harness image data and crimping geometric data in real time based on the dynamically adjusted light source angle and intensity; the visual enhancement fusion module is used for de-noising the wire harness image data by using a non-local mean de-noising algorithm and dynamically adjusting and controlling a similarity measurement range through a smooth factor; superposing a multi-scale Retinex algorithm, and enhancing the texture contrast of the wire harness image data through the local contrast; the wire harness image data and the crimping geometric data are fused, and a crimping part multi-mode characteristic graph is constructed; the accuracy of defect detection is facilitated, and potential problems can be found and solved in time.

The invention discloses a waste plastic classification method and system based on visual identification, and particularly relates to the technical field of plastic classification. Reflection suppression is realized through an industrial camera equipped with a polarization filter and a multi-angle cross polarization light source, and the structure and color characteristics of a shielded area are effectively restored by combining brightness equalization, reflection area detection and image restoration technologies; and then semantic segmentation and target classification are completed by using an improved deep neural network model, a control signal is generated based on an identification result, and an execution mechanism is driven to realize accurate sorting of multiple types of plastics, so that the identification robustness and the sorting efficiency of the system under a complex illumination condition are improved.

The invention discloses a magnetic material defect detection system based on image recognition, which adopts a first acquisition module, a first determination module, a second determination module, a second acquisition module and a third acquisition module, and is characterized in that the first acquisition module is used for performing multi-angle illumination control on the surface of a magnetic material by adopting a polarized light imaging device; mirror reflection generated by the strong light reflection characteristic is inhibited by adjusting the angle of a polaroid and the incident angle of a light source, original image data with uniform illumination distribution are obtained, and if it is detected that an overexposure area exists in an image, exposure parameters and the polarization angle are automatically adjusted to obtain a high-quality image suitable for follow-up processing; and the first determination module is used for performing multi-scale wavelet transform decomposition according to the acquired high-quality image, identifying a surface defect area by analyzing texture features and edge information in different frequency components, and judging that the surface defect area is a potential defect area if a wavelet coefficient in the high-frequency component exceeds a preset threshold value. The detection performance is obviously improved.

A gaming device that includes a handheld controller having a handle and a depressible button on the handle configured to receive user input. The depressible button includes a first marking layer including a first indicia, a first light source configured to illuminate the first indicia in the first marking layer, a second marking layer stacked with the first marking layer and including a second indicia, and a second light source configured to illuminate the second indicia in the second marking layer.

The invention discloses a package defect elimination method based on visual inspection, and relates to the technical field of image processing and computer vision, and the method comprises the following steps: setting a polarized light source with a fixed angle on a package production line, and uniformly illuminating a target package area to enable the incident light polarization direction and the package surface reflection direction to form a deviation angle; an industrial camera provided with a polarization filter is used for carrying out image collection on a packaging target area, and the polarization direction of the filter is perpendicular to the polarization direction of a light source so as to restrain reflection interference to the maximum extent. Reflection interference is eliminated through polarized light, and the image quality is improved; by combining a neural network and feature fusion scoring, the defect recognition accuracy and adaptability are improved; a closed-loop elimination control and data tracing mechanism is introduced, the elimination precision is guaranteed, quality responsibility investigation is supported, and the intelligence and reliability of the packaging defect detection and elimination system are integrally improved.

The invention provides a modified metal surface defect detection method and device and a medium, and the method comprises the steps: obtaining a multi-angle reflection image sequence of a modified metal surface under the irradiation of a multi-spectral light source, and generating a defect sensitive parameter set based on a preset modified metal material characteristic database and in combination with the spectral reflectivity distribution information of the multi-angle reflection image sequence; performing cooperative feature enhancement on the multi-angle reflection image sequence and the defect sensitive parameter set, and enhancing the feature contrast of a defect area and a normal area through weight distribution to obtain an enhanced defect feature set; joint anomaly detection of a spatial domain and a spectral domain is carried out on the enhanced defect feature set, a potential defect region of the modified metal surface is obtained through identification, and a defect region feature descriptor is generated; and performing defect morphological quantitative analysis according to the defect region feature descriptors, and determining the type, position and severity level of the modified metal surface defect. According to the invention, the comprehensiveness and reliability of a defect detection result can be improved.

The invention discloses a pipe online defect detection method and system, and relates to the technical field of image detection. The detection method comprises the following steps: placing a to-be-detected pipe in a dark box environment, irradiating the to-be-detected pipe in a rotating state through a light source, displaying a pipe projection on a projection surface, and setting a detection line at the upper end of the projection surface; comparing the detection line with a set reference line in real time, and marking a suspected warping area according to the local offset; performing three-dimensional image reconstruction on the suspected warping area to generate a simulation three-dimensional point cloud model; the method is technically characterized in that preliminary judgment passes through a grading strategy of'rapid screening-precise reinspection ', and the detection speed and precision are balanced; by means of the optical path principle and threshold value comparison, positioning operation of the whole-surface suspected warping area can be rapidly completed, only data of a local area (suspected warping area operation) is analyzed, the hardware cost and energy consumption are greatly reduced, and the high-speed online detection requirement is met.

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 provides an intelligent cooperative control method and system for a multi-mode phototherapy device, and the method comprises the steps: obtaining physiological parameters of a plurality of users to form a data set, and constructing a spectrum feedback model in combination with historical phototherapy data and environment data; on the basis of the model and historical phototherapy target data, a multi-mode spectrum parameter model is generated through a preset neural network, and cooperative control over multi-light-source equipment is achieved; physiological parameter changes of a target user are monitored in real time, and monitoring data are sent to an edge computing node; the edge node dynamically adjusts a spectral parameter combination scheme according to the monitoring data and a safety threshold, and generates a treatment effect evaluation index; and establishing a user spectrum feedback feature library based on the evaluation indexes, and continuously optimizing a spectrum feedback model through incremental learning. According to the invention, intelligent cooperative control of the multi-mode phototherapy equipment can be realized, the energy utilization efficiency of the phototherapy equipment is improved, the spectrum feedback difference influence among different users is reduced, and the accuracy and safety of the phototherapy effect are enhanced.

Embodiments of the present invention provide a phototherapy mask designed to enhance user comfort and effectiveness during treatment. The mask comprises a shell that fits over the user's face, featuring a first opening to avoid contact with the eyes and a second opening to avoid the nose. A first pad, positioned around the edge of the first opening, rests against the skin near the peripheral eyes to control the distance between the mask and the skin, while a second pad, located at the bottom of the shell, supports the chin. These pads help maintain the mask's stability and prevent it from shifting during use. The light source assembly, installed on the shell, provides phototherapy to the skin.

The invention relates to the technical field of image recognition, in particular to a circuit board micropore copper plating quality inspection method based on image recognition. The method comprises the following steps: setting an annular polarized light source array; performing dynamic depth-of-field fusion shooting on the circuit board micropore based on the annular polarized light source array to obtain a two-dimensional projection drawing of the circuit board micropore; performing sub-pixel edge enhancement on the circuit board micropore two-dimensional projection drawing to generate a circuit board micropore enhanced projection drawing; and segmenting the interest boundary of the circuit board micropore enhanced projection drawing to obtain the hole wall region-of-interest boundary. Advanced image recognition, deformation analysis and self-learning technologies are introduced, efficient, accurate and automatic circuit board micropore copper plating quality intelligent inspection is achieved, and the defects of a traditional method in the aspects of detection precision, efficiency and comprehensive evaluation capacity are overcome.