1126 results about "Illuminance" patented technology

The invention discloses an image enhancement method for brightness gain self-adaptive regulation and control under low illumination, and relates to the technical field of image data processing. The image enhancement method for brightness gain adaptive regulation and control under low illumination comprises the following steps: S1, acquiring original image data and auxiliary processing data under a low illumination environment for preprocessing, and constructing an image processing database after storing the original image data and the auxiliary processing data; s2, performing illumination distribution optimization and brightness correction operation through brightness deviation analysis, and outputting an illumination enhancement component; s3, performing de-noising and detail enhancement processing according to a noise color deviation result, and outputting a corrected reflectivity component; s4, through image quality enhancement analysis, using a loss function to guide a double U-Net architecture to carry out detail reconstruction and color optimization on the image; and S5, based on the quality feedback and the difficult sample, adjusting processing parameters and driving algorithm closed-loop optimization. The problems of insufficient image brightness, noise interference, color distortion and detail missing multiple degradation in a low-light environment are solved.

The invention provides a low-illumination perception method and system based on space-frequency fusion, and relates to the technical field of image processing, and the method comprises the steps: obtaining a normal light image and a low-illumination image corresponding to the normal light image; performing feature extraction on the normal light image and the low-illumination image through an encoder to obtain multi-scale spatial features; decomposing the multi-scale spatial features through stationary wavelet transform to generate a low-frequency feature component and a high-frequency feature component; performing optimization processing on the low-frequency characteristic component and the high-frequency characteristic component based on a parameter learnable frequency domain adaptive filtering mechanism; fusing the optimized low-frequency feature component and the optimized high-frequency feature component by introducing a cross attention learning mechanism to obtain a frequency domain feature; performing adaptive complementary fusion on the frequency domain features and the multi-scale spatial features to generate fusion features; step-by-step up-sampling is carried out on the fusion features through a decoder; and outputting a segmentation result of the low-illumination image through a sensing head according to the fusion features after up-sampling.

The invention discloses an illumination adaptability rock crack identification and calibration method based on an image identification algorithm, and relates to the technical field of geotechnical engineering intelligent image identification, and the method comprises the following steps: collecting an illumination incident angle, a polarization state and an illumination gradient corresponding to an image frame, building an illumination time sequence observation layer, and obtaining an illumination time sequence observation layer; forming an illumination feature baseline for crack boundary tracking; and performing coherent phase decomposition on the image sequence based on the illumination feature baseline, stripping mirror reflection and enhancing diffuse reflection texture, and generating a crack drift suspicion image. Through construction of an illumination time sequence observation layer and coherent phase decomposition, illumination interference stripping in a crack image is realized, false drift is eliminated by combining anti-fact playback and time reconstruction, and a dynamic threshold control mechanism is constructed by introducing an anti-illumination mask and an anti-phase pattern and linking a polarized light diaphragm and a shading array. And high robustness of crack identification and closed-loop regulation and control of false early warning are realized.

The invention provides a lamp layout method and a lamp, and relates to the technical field of intelligent lighting. Personnel activity data are generated by acquiring real-time signal data acquired by a sensor in a target space and performing clustering analysis and behavior recognition. And generating target illumination and color temperature data of each region through biological rhythm correlation modeling and time sequence prediction based on personnel activity data and historical rule data. And performing multi-target weight dynamic optimization and improved genetic algorithm solution by adopting the generated data and personnel movement characteristics to obtain a lamp control strategy. And performing priority division and differential driving adjustment in combination with the real-time distribution of the personnel and the aging state of the lamp, and generating illumination feedback data. And iteratively executing adjustment based on the feedback data, the target illuminance, the color temperature data and the adjacent lamp collaborative error optimization control parameters. Illumination distribution is optimized by dynamically adapting to personnel activity rules, real-time illumination requirements can be accurately matched to reduce energy consumption and improve utilization efficiency, and scene adaptability can be considered.

The invention discloses an image edge feature enhancement correction fusion method based on a guide filter, and aims to solve the problems that detail features of a low-illumination visible light image and an infrared image are not obvious, focusing edge information is not clear, and registration of a multi-focus image is wrong. The invention provides an edge feature enhancement correction fusion method based on a guide filter. In the illumination enhancement stage, a visible light image is divided into a base layer and a detail layer through a guide filter, and the image contrast and detail information are enhanced. For an infrared image, an infrared background is reconstructed by using a quadtree decomposition and Bezier interpolation method, unclear focusing edge feature information is extracted, and the visibility of image details is enhanced. And finally, reconstructing the two processed images by using a multi-scale weighted gradient method, and performing fusion by calculating large-scale and small-scale weight scales.

The invention discloses a high-end space light illumination control system with scene adaptability, and relates to the technical field of intelligent light control, and the high-end space light illumination control system is characterized in that spatial structure geometric information and surface material distribution are acquired, and an illumination path simulation model is constructed; in combination with a preset illumination demand template, identifying a position with illumination deviation; performing illumination coverage simulation on each position point based on the lamp type and the light emitting angle, calculating an illumination coverage contribution value, and constructing a reference matrix; according to a matrix result, lamp model selection and angle adjustment are carried out, partition control operation is executed, and dynamic optimization adjustment of the brightness, the angle and the color temperature is achieved; closed-loop correction is completed through illumination feedback; according to the invention, the adaptive ability of illumination control to a complex space structure and a dynamic scene is improved, higher control precision and energy efficiency are achieved, and the method is suitable for intelligent illumination systems in scenes such as exhibition display, commercial retail and high-end residence.

The invention discloses an LED (Light Emitting Diode) energy-saving street lamp system self-adaptive to an ambient light effect and an illumination adjusting method thereof, and belongs to the technical field of novel illumination control and green energy conservation. The system comprises a light environment sensing module, a traffic flow monitoring module, an LED driving power supply and an electroluminescent illumination module composed of a plurality of light emitting diodes. The main control module is configured to execute active lighting effect decoupling logic, namely, when the electroluminescent lighting module keeps steady-state lighting, the driving power supply is controlled to inject a micro-disturbance modulation signal which cannot be perceived by human vision into output current, and illuminance response data of the light environment perception module are synchronously collected; the road surface reflectivity coupling coefficient is obtained in real time by calculating the change ratio of the response data to the modulation signal, then the reflected light component excited by the street lamp itself is accurately removed from the total illumination value, and the pure environment background illumination is decoupled. The problems of misoperation and energy waste caused by self-luminous interference of a traditional light-operated street lamp are solved.

The invention relates to the technical field of intelligent scheduling of energy-saving lamps, in particular to an intelligent lighting prediction and scheduling system based on big data, which comprises a lighting behavior extraction module, a lighting feature division module, a regional lighting partition module, a traffic illumination linkage module and a scheduling execution integration module. The method comprises the following steps: extracting an activation frequency and path approaching relation, constructing a state sequence and a rhythm index, dividing feature fragments to generate a distribution diagram, sorting combined fragments to generate a response queue, and adjusting an illumination sequence to generate a scheduling list. According to the method, a rhythm index is constructed by collecting an activation frequency and path approaching relationship, illumination fragments are divided based on a feature deviation degree, a region linkage sequence is sorted in combination with time coverage and space continuity, the priority of lagging fragments is adjusted, and scheduling entries containing traffic trigger intensity and response advance rate are output. The adaptive response of the lighting rhythm to the path change is realized, the coordination precision and the control consistency are improved, and the energy consumption and the sensing error are reduced.

The invention discloses a vehicle blind area dynamic enhancement and warning system for a low-light environment, and relates to the technical field of automobile active safety, and the system comprises a short wave infrared imaging module which is used for capturing a passive thermal feature image of a scene behind the side of a vehicle; the multi-source signal acquisition module is used for acquiring a vehicle bus signal representing a vehicle motion state and an operation intention of a driver in real time; the data processing and risk assessment module is connected with the short wave infrared imaging module and the multi-source signal acquisition module, and is used for performing fusion processing on the thermal feature image and the vehicle bus signal, identifying a blind area target and calculating a dynamic risk level of the blind area target; the grading warning and intervention module is connected with the data processing and risk assessment module; the short-wave infrared imaging technology which is sensitive to night light and high in anti-interference capacity is adopted, the imaging problem under extremely low illumination is fundamentally solved, and stable detection of vehicles and pedestrians in a long distance in a completely light-source-free environment is achieved.

The invention provides a hydraulic power plant medium leakage identification method and system, and relates to the technical field of hydropower leakage identification, and the method comprises the steps: deploying a high-definition video monitoring device and an operation state sensor module, carrying out the synchronous registration, configuring infrared channel enhancement and polarized light interference suppression, and carrying out the spatial filtering noise reduction and adaptive adjustment; and configuring a multi-modal enhanced input tensor. High-definition video monitoring equipment and an operation state sensor module are deployed in an operation area of a hydraulic power plant, and technologies of infrared channel enhancement, polarized light interference suppression, spatial filtering noise reduction and the like are combined, so that a multi-mode enhanced input tensor is constructed; according to the method, a clear leakage area identification effect can still be kept in a night low-illumination environment, water vapor and fog interference and metal reflection conditions, so that the problem that the identification precision is reduced due to the fact that the prior art depends on single-mode input is effectively solved, and stable distinguishing of oil medium leakage and water medium leakage is achieved.

The invention relates to a low-illumination image enhancement method based on multi-mode classification and brightness feedback, and belongs to the field of image processing. The method comprises the following steps of: firstly, training a network, constructing a multi-modal illumination prior feature of an image, inputting the multi-modal feature and a brightness score of the image into an illumination perception classification network, obtaining a local probability value of image brightness, adaptively selecting a local or global enhancement processing method, and generating a fusion weight; in the local enhancement processing, dark area details are enhanced through a multi-scale parallel and double-attention mechanism, in the global enhancement, the brightness is improved by using a symmetric coding-decoding structure and a residual attention block, and linear fusion is performed after the brightness is enhanced; then, the brightness evaluation value of the image is fed back through the lightweight brightness estimation network, and the brightness of the image is enhanced. And optimizing the training network according to the joint loss function of image processing. The method provided by the invention can enhance image texture details and improve image definition under a non-uniform illumination condition and an extremely low illumination condition.

The invention relates to a tunnel partition illumination dynamic optimal control method and system based on traffic flow space-time distribution prediction, and belongs to the technical field of tunnel illumination energy-saving operation. The method aims to solve the problems of black hole effect caused by response lag, trailing illumination caused by rough partition and lack of pre-judgment ability in the existing control technology. According to the technical scheme, data are collected through a multi-source traffic perception layer, and advanced prediction is carried out by fusing a macroscopic and microscopic model and deep learning through a traffic flow space-time trajectory prediction layer; the dynamic partition optimization decision-making layer maps a prediction result into a virtual light packet and solves an optimal dimming instruction meeting illuminance, uniformity and vehicle speed correlation type brightness change rate constraints through model prediction control, and finally the optimal dimming instruction is output through the smooth execution and closed-loop correction layer. According to the system, the black hole effect can be fundamentally eliminated, the safety is improved, fine energy-saving control over the lamp moving along with a vehicle is achieved, the visual comfort is guaranteed, and the system has high adaptability and robustness for multiple traffic scenes.

The invention relates to the technical field of intelligent illumination control, and discloses an intelligent enhancement method and system for the brightness of an LED light-emitting module, and the method comprises the steps: collecting environment illumination data and LED module operation state data, and carrying out the preprocessing; acquiring cultural relic material information, performing exhibit change detection, determining an illumination constraint threshold, calculating effective illumination and tracking accumulated exposure; determining an illumination safety boundary and dynamically modulating an LED spectrum; performing hierarchical coordination by adopting a three-layer game decision framework; a three-dimensional light field representation framework is constructed through audience behavior perception and visual attention prediction, and space illumination optimization is carried out; environment sudden changes and audience behaviors are detected, and quick response adjustment is executed; analyzing lighting space-time distribution characteristics, performing dynamic power distribution and generating an intelligent dimming curve; according to the invention, environmental perception, cultural relic protection constraint, light attenuation compensation, multi-module cooperation and intelligent decision can be comprehensively considered.

The agricultural condition integrated monitoring device comprises an electric pickup truck serving as a vehicle-mounted platform, an upper-layer mounting plate is mounted at the top of a cargo compartment of the electric pickup truck, an unmanned aerial vehicle cabin is arranged on the rear portion of the upper end face of the upper-layer mounting plate, and an insect condition station is arranged on the front portion of the upper end face of the upper-layer mounting plate; a vehicle-mounted holder ball machine is installed on the top of the insect situation station, a wind direction sensor, a wind speed sensor, a photosynthetically active radiation sensor and an illuminance sensor are installed on the top of a cockpit of the electric pickup truck through corresponding profile supports, and a server square cabin is arranged between the lower end face of an upper-layer installation plate and the interior of a cargo compartment and used for data exchange, transmission and storage among multiple devices. And carrying out real-time calculation processing and fusion analysis on the multi-source data. The unmanned aerial vehicle cabin, the four-condition monitoring equipment and the edge computing or server module are uniformly integrated on the vehicle-mounted platform, so that the cooperative operation of the vehicle-mounted intelligent unmanned aerial vehicle cabin and the unmanned vehicle-mounted platform and the high-performance data processing and remote management are facilitated.

The invention provides an urban lighting intelligent management and comprehensive evaluation method and system, and relates to the technical field of urban intelligent lighting and energy-saving control, and the method comprises the steps: collecting a basic lighting parameter set of a road of an urban lighting system, and calculating basic lighting calculation data; collecting calibration parameters of the lighting system after the lighting loop enters a stable working state; acquiring an illumination data set and an electric power parameter set of the illumination loop and outputting a real-time illumination data set; calculating an urban comprehensive lighting rate, an urban weighted average illumination value, a theoretical illumination power density coefficient, an actual illumination output coefficient and an illumination system comprehensive energy efficiency ratio, taking the urban comprehensive lighting rate as an urban lighting rate statistical result, and taking other calculation results as a multi-dimensional energy efficiency index system; and establishing a lighting rate-energy efficiency index correlation model, identifying abnormal and low-efficiency lighting scenes, and generating personalized urban lighting optimization suggestions, thereby realizing double accurate control of the lighting state and lighting energy efficiency of the urban lighting system.

The invention relates to the technical field of semantic segmentation, in particular to a night semantic segmentation method based on low illumination enhancement and edge optimization, and the method comprises the steps: inputting an image into a low-light enhancement repair network based on the Retinex theory, local contrast enhancement and adaptive feature fusion, obtaining a denoised and enhanced intermediate image, and carrying out the edge optimization of the intermediate image; inputting the intermediate image into a semantic segmentation network to obtain a category distribution diagram of each pixel; inputting a discriminator embedded with a channel attention module according to the category distribution diagram of each pixel, and optimizing a generator composed of a low light enhancement repair network and a semantic segmentation network through a multi-task joint optimization loss function; and inputting a night image to be detected and segmented into the optimized generator, and outputting a segmentation result. By adopting the method, the low-light enhancement repair network is combined with a local contrast enhancement and channel feature fusion mechanism, so that the overall brightness of the image is improved, the details and edge structures of the image are reserved, and the perception capability and robustness of the model are improved.

The invention discloses a low-illumination blurred image 3D scene reconstruction method based on Gaussian sputtering, and the method comprises the steps: S1, building a progressive iteration enhancement frame, and setting a middle brightness anchor point between low-light observation and target brightness; s2, generating a plurality of enhanced images based on the intermediate brightness anchor points in combination with histogram equalization and gamma correction technologies; s3, carrying out rapid deblurring processing on the enhanced image; s4, constructing a scene representation model based on 3D Gaussian sputtering, and performing explicit estimation and noise suppression in combination with a noise sensing module; s5, taking the reconstructed rendered image as the deblurring prior of the enhanced image of the next brightness level so as to execute deblurring processing operation, and performing iterative optimization until the target brightness is reached; and S6, generating a high-quality new view angle image based on the finally reconstructed 3D scene. According to the method, the rendering speed is greatly improved while the reconstruction quality is ensured, real-time three-dimensional reconstruction is realized, and the problem of noise amplification in a low-light environment is effectively solved.

The invention relates to a passive multiband sensing array system and a multi-target tracking method. The system comprises a multiband sensor group, an environment illumination sensor, a synchronization and control unit and a preprocessing unit, the multi-band sensor group comprises a visible light sensor, a near-infrared sensor and a long-wave infrared sensor which are integrated in a unified shell through a coaxial light path and do not comprise active emitters; the synchronization and control unit generates a synchronization signal and controls the multi-band sensor group and the environment illumination sensor to perform synchronous data acquisition; and the preprocessing unit receives the original image data, the environment illumination data and the synchronizing signal, outputs a structured multi-mode data packet and provides the structured multi-mode data packet to the multi-target tracking engine at the rear end, and the multi-target tracking engine outputs the tracking states of multiple targets. According to the invention, stable, continuous and high-precision tracking of a plurality of targets can be realized in a plurality of complex scenes, and the problems of poor environmental adaptability, dependence on active emission and insufficient robustness of the existing multi-target tracking scheme are effectively solved.

An electronic device may include: a display, a light sensor, and at least one processor configured to obtain a first illuminance value based on illuminance data obtained through the light sensor for a first time during which the display is turned on and off, obtain a second illuminance value based on illuminance data obtained through the light sensor for a second time shorter than the first time, while the display is turned on and off, determine a third illuminance value based on the first illuminance value and the second illuminance value, determine a fourth illuminance value based on color information about an image displayed through the display and information related to a luminance of the display, and determine a correction value for correcting an illuminance value obtained through the light sensor based on the third illuminance value and the fourth illuminance value.

The invention relates to the technical field of artificial intelligence, and discloses a low-illumination scene-oriented unmanned aerial vehicle imaging data enhancement method, which comprises the following steps of: S1, acquiring unmanned aerial vehicle imaging data and constructing a training data set; s2, data preprocessing based on illumination partition; s3, constructing and training a data enhancement model; and S4, enhancing the imaging data of the unmanned aerial vehicle. The data preprocessing based on the illumination partition in the step S2 comprises the following steps: generating a low-illumination area mask and a medium-illumination area mask based on the brightness information of the low-illumination image; a Gaussian smooth convolution operation is applied to the low-illuminance region identified by the low-illuminance region mask to suppress noise, and an original value is reserved for the medium-illuminance region identified by the medium-illuminance region mask to generate a pre-processed image. The problems of low-illumination image noise amplification and detail loss are solved by generating low-illumination region masks and medium-illumination region masks and respectively adopting noise reduction and retention strategies, and the balance of dark region noise reduction and bright region detail protection is realized.

The invention discloses a low-illumination image enhancement method based on concave manifold projection and local entropy weight arbitration, and belongs to the technical field of digital image processing. According to the method, a deep enhancement network of concave manifold projection and local entropy weight arbitration is constructed, and the network comprises an encoder module and a decoder module. In an encoder part, a concave manifold projection mechanism based on physical constraints is introduced, a four-dimensional input vector is constructed by extracting a texture density map and an RGB channel, physical parameter features are predicted by using a semantic guidance type physical manifold parameterization module, and an illumination enhanced parameterization model is established to reconstruct illumination distribution. In a decoder part, a local entropy weight arbitration mechanism is introduced, multi-scale features are captured by using a group of parallel cavity convolution branches with different expansion rates, and adaptive weighted fusion is performed on the features through a space attention weight map generated by a local entropy guidance type multi-scale arbitration module, so that noise amplification is suppressed while image details are recovered. According to the invention, enhancement of the low-illumination image can be effectively realized.

The invention discloses a night enhanced imaging and dynamic denoising method for an automobile data recorder, relates to the technical field of vehicle-mounted image processing and computational photography, and is used for solving the problems of insufficient brightness and too strong noise of night videos of the automobile data recorder under a low-illumination condition. Estimating the zero-mean Gaussian noise intensity of a read link and the Poisson noise intensity increasing along with the brightness in the same frame, converting the zero-mean Gaussian noise intensity and the Poisson noise intensity into brightness equivalent parameters, and introducing a unified control function G as the scale reference of all enhancement and noise reduction links; defining a sharing priori H and carrying out unified constraint in dense optical flow, depth time sequence denoising and local tone mapping; and in combination with the motion mask and the background model, performing differential space-time denoising and proportional write-back, and in cooperation with motion blur detection, spatial variation motion kernel and constrained deconvolution and multi-frame detail recharge, obtaining target brightness and outputting an RGB frame after proportional write-back.

A method for operating a projection exposure system (1) comprises the following steps: providing a projection exposure system (1) with a radiation source (1) for generating illumination radiation (3), an illumination optic (11) for transferring the illumination radiation (3) from the radiation source (2) to an object field (8) extending in a scan direction (y) and a cross-scan direction (x), wherein the illumination optic (11) has at least one faceted mirror (6, 7) with a plurality of individual mirrors, and wherein at least a subset of the individual mirrors is configured such that they only partially illuminate the object field (8) in the scan direction (y), and a projection optic (10) for imaging a reticle (12) arranged in the object field (8) of the illumination optic (11) onto a wafer (19) arranged in an image field (17) of the projection optic (10), and specifying a set of at least one element of field-dependent aberrations. (Ai(x, y);i=(1..M)), specification of a target vector (Z(x) = (Z1(x), ..., Z; M (x) T ) for the target values ​​Z i (x) of the aberrations Ai(x, y) after averaging over the scan direction (y), dividing the extent of the object field (8) in the scan direction (y) into j ≥ 2 intervals ([y0; y1], ..., [y j-1 ; y j ]) and determining scan weights (gi(x, y)) which characterize a dependence of an illuminance at a location x in the cross-scan direction in the object field () on a position in the scan direction (y) such that a deviation of a weighted aberration vector A weighted ( x ) → : = ∑ gi A l → is reduced from the target vector (Z(x)).

The invention discloses an intelligent enhancement method for a road image under a low-illumination condition, and belongs to the technical field of computer vision and video processing, and the method comprises the following steps: S1, calculating an actual illumination value of a current road scene; s2, adaptively adjusting the exposure time; s3, targeted noise reduction; s4, color temperature correction; s5, enhancing the image contrast; s6, performing strong light suppression to obtain a final current road scene image; under extremely low illumination, the image signal-to-noise ratio is increased by more than 40%, the color rendition degree is larger than or equal to 90%, clear full-color images can be output without external light supplementation, meanwhile, the strong light inhibition rate of vehicle headlights and the like is larger than or equal to 95%, the overexposure area is reduced to be lower than 5% of the original area, the algorithm processing delay is smaller than or equal to 40 ms, the real-time monitoring requirement is met, and the method is suitable for highway night scenes.

The invention belongs to the field of exhibition light control, and particularly relates to an exhibition space light environment parameter optimization regulation and control method and system based on illumination perception, and the method comprises the steps: firstly building a three-dimensional model through obtaining exhibition semantic information and spatial constraints, extracting an exhibit light sensitivity level and spatial fluctuation information, and dividing an illumination control region sequence according to the exhibit light sensitivity level and the spatial fluctuation information; fusing the photosensitive damage rate and the importance degree of the exhibit, and determining a dynamic target optical parameter control interval set; the method comprises the following steps: constructing a hierarchical target mapping function of a fusion fit degree, exhibition experience and an energy-saving index, and dynamically setting main and auxiliary control functions in different time periods according to a control index priority factor; and solving the optimal optical parameter set of each time period by adopting an optimization search algorithm, and carrying out inversion to obtain an optical parameter control index. According to the method, self-adaptive balance optimization of the exhibition light environment among multiple targets of cultural relic preventive protection, audience visual experience and system energy-saving efficiency is realized.

The invention discloses a low-illumination image enhancement method combining multi-channel parallel attention and cross fusion, and relates to the field of photoelectric display. In order to solve the problems of high model complexity, brightness and color gamut information coupling, artifact fusion and the like in low-illumination image enhancement, the invention provides a lightweight low-illumination image enhancement network, brightness and chrominance information are separated in a YCbCr color space, brightness structure features are extracted through a lightweight denoising module and a multi-head attention mechanism, and a low-illumination image is obtained. And a multi-channel parallel attention module (MCPA) is designed to enhance the chromaticity context modeling capability. According to the invention, a brightness-guided cross fusion module (LCA) is introduced, and cooperative enhancement of the structure and the color is realized through joint channel-space attention optimization. According to the method, a relatively high image quality index is obtained on multi-class public low-illumination data, meanwhile, the parameter quantity and the calculation quantity are remarkably reduced, and the method is suitable for edge device deployment. According to the method, color cast and fusion artifacts can be effectively inhibited, and the consistency of structural details and colors is improved.

The invention discloses a low-illumination visible light and infrared image fusion method with color recovery capability, and belongs to the technical field of computer vision. The method comprises the following steps: designing a low-light enhancement loss function based on comparative learning, so that the color distribution of an enhanced visible light image is aligned with the color distribution of an ideal image; designing a low-light enhancement neural network based on a Mangbar structure, converting the low-illumination image into a YCbCr color space, decoupling an illumination component and a color component of the low-light image, and respectively implementing enhancement processing; a fusion network based on a Mangban structure is designed, modal complementary information is obtained through a mixed attention mechanism, the enhanced visible light image and the infrared image are fused, color information of the fused image can be effectively recovered, and the visual quality of the fused image is improved.

The invention discloses a control system for detecting light attenuation and self-compensation of an LED heating lamp panel, and relates to the field of semiconductor wafer heating and cleaning, the system collects illumination data of the LED heating lamp panel through a spectrum detection unit, a main control unit compares real-time illumination data with target illumination data, and then the input power of the lamp panel is adjusted to realize self-compensation; the temperature detection unit and the current detection unit respectively monitor the temperature of the lamp panel and the current of the power supply loop, and stop the self-compensation operation in case of abnormality; after illumination collection or power adjustment is completed every time, the lamp panel enters a zero-power input stage to be cooled; after the self-compensation is completed, the main control unit transmits the adaptive power value to the equipment upper computer so as to update the process parameters; according to the system, automatic detection and accurate compensation of light attenuation of the LED heating lamp panel are achieved, manual intervention is not needed, the stability of the light power density of the LED heating lamp panel is guaranteed, the equipment loss is reduced, and the wafer cleaning quality and the production efficiency are improved.

The invention discloses a low-light image target detection method based on enhanced feature fusion, and belongs to the field of computer vision and target detection. According to the method, multilayer features are extracted by using a backbone network, a bidirectional multi-scale feature pyramid and three-path convolution fusion module is designed, shallow features and transverse jump connection are introduced in the fusion process, and efficient combination of spatial details and semantic information is realized; an intra-scale Transform encoder is adopted to enhance the feature expression ability, and in a decoding stage, a deformable attention and query selection strategy is combined, so that the detection efficiency and precision are improved; and end-to-end training is realized through Hungary matching and set loss. According to the invention, real-time high-precision target detection can be realized in a low-illumination environment, and the method is suitable for night vision monitoring and intelligent sensing scenes.

The invention relates to an automatic testing device and method for the optical performance of an operating shadowless lamp and a control system, and the device comprises a shadowless lamp fixing rack which is used for detachably fixing the operating shadowless lamp to be tested in a horizontal installation manner, and enabling the optical axis of the operating shadowless lamp to be horizontally arranged; the moving rack and the operating shadowless lamp fixing rack are arranged in a split mode; the three-dimensional linear module is mounted on the moving rack; the illuminometer is mounted on the moving end of the three-dimensional linear module through a fixing device; the control unit is in communication connection with the driving motor of the three-dimensional linear module and the illuminometer; wherein the control unit is configured to execute an automatic test step, and the automatic test step comprises the steps of controlling the illuminometer to move according to a preset scanning track so as to collect illumination data, and calculating optical performance parameters of the operating shadowless lamp based on the data. The operating shadowless lamp optical performance testing device achieves efficient, high-repeatability and high-precision automatic testing of the optical performance of the operating shadowless lamp, and is particularly suitable for rapid quality inspection on a production line.