350 results about "Low level light" patented technology

Systems and methods for training a neural radiance field model for noisy scenes can leverage raw noisy images in linear high dynamic range color space to train a neural radiance field model to generate view synthesis of low light and / or high contrast scenes. The trained model can then be utilized to accurately complete view rendering tasks without the preprocessing used for generating low dynamic range images. In some implementations, training on unprocessed data of a low light scene can allow for training a neural radiance field model to generate high quality view renderings of a low light scene.

The invention relates to the technical field of display, and particularly discloses a dynamic ambient light adaptive OLED display optimization method and system, and the method comprises the steps: obtaining an ambient light time sequence data set of an environment where an OLED display screen is located, and synchronously obtaining real-time display parameters and historical brightness adjustment data of the display screen; acquiring ambient light sensing features according to the ambient light time sequence data set; obtaining photo-thermal structure coverage area data of the OLED display screen according to the real-time display parameters; and acquiring preliminary optimization parameters according to the ambient light sensing characteristics and the photo-thermal structure coverage area data. According to the invention, illumination intensity, spectrum drift and contrast change are captured in real time in combination with ambient light perception characteristics, the one-sidedness of adjustment only according to brightness traditionally is solved, reflection can be reduced through photothermal structure deformation under strong light, brightness can be improved to offset interference, and human visual characteristics can be matched under weak light. And the display definition and the visual comfort in different environments are effectively improved.

The invention discloses a weak light image enhancement method fusing noise adaptive diffusion and illumination perception, and the method comprises the steps: firstly designing a reflection-illumination decomposition module RID based on a Retinex theory, and extracting a reflectivity component and an illumination component of an input weak light image; then designing an ADRD (adaptive diffusion reflection denoising) module, performing denoising enhancement on the reflectivity component, and outputting an enhanced reflectivity image; introducing a noise estimation network, and dynamically adjusting the de-noising amplitude of the diffusion model according to the noise intensity of the region in the reflectivity graph; designing an illumination perception enhancement module IEM, fusing an attention mechanism and a local enhancement block, enhancing the illumination image, and outputting an enhanced illumination component; and building a complete neural network framework, carrying out joint training on the provided module, finally testing the stored neural network model, and outputting an enhanced image. According to the method, the problems of detail loss, noise residue and uneven illumination of the enhanced image in the prior art are solved.

The invention discloses an optical performance detection system and method for a camouflage material, and relates to the technical field of camouflage material detection, and the system comprises a structure identification and illumination control module which is used for collecting a surface image of a camouflage material sample, identifying the surface structure of the sample, starting a full-spectrum light source, and simulating step-by-step illumination from weak light to strong light; carrying out RGB imaging based on the standard illumination; and the region division module is used for applying a superpixel segmentation algorithm to the image, automatically dividing sub-regions, performing hyperspectral reflectivity scanning on each region, synchronously acquiring a light intensity image in a polarization direction, calculating a Stokes vector and constructing a polarization enhanced image. According to the method, frequency domain quantitative analysis of microcosmic interference fringes and accurate mask extraction of polarization abnormal areas can be realized, optical exposure risks of camouflage materials under complex illumination and visual angle conditions are effectively quantified through multi-angle reflection sampling and scoring function modeling, and the detection precision, the area pertinence and the automation level are remarkably improved.

The invention discloses an automobile central control screen small target detection method based on YOLOv11 improvement, and the method specifically comprises the steps: S1, generating an image data set, carrying out the preprocessing and enhancement, and dividing the data set; s2, an improved C3k2GCConv module, a WFU module and a CGAFusion module are introduced, and a YOLOv11 network model is constructed; s3, training the model by adopting a cosine annealing learning rate and a mixed precision training strategy; s4, inputting a to-be-detected central control screen image into the improved YOLOv11 detection model, and outputting the category and bounding box coordinates of a target; and S5, performing screening and optimization through a post-processing module, and finally outputting a detection result in the form of a bounding box and a category label. According to the method, by improving the YOLOv11 model, a display target can be effectively recognized in complex environments such as strong light direct incidence, screen reflection and dim light, and the stability and robustness of the model in a complex illumination scene are improved.

The invention discloses a method for detecting and tracking a dynamic obstacle in a weak light environment. The method comprises the following steps: 1) acquiring an image; (2) a U-depth detector and a DBSCAN detector are used for conducting rapid detection on a near obstacle, and after weak light images of a far obstacle are enhanced through a lightweight image enhancement network LPF, a lightweight FastDet model is input to conduct deep learning auxiliary detection; 3) calculating the intersection-to-union ratio of the U-depth detector, the DBSCAN detector and the LPF-FastDet detector, fusing the intersection-to-union ratio results of the U-depth detector, the DBSCAN detector and the LPF-FastDet detector, and generating a set bounding box; 4) performing data association on the detected obstacles through a feature association method, and performing continuous tracking by using Kalman filtering; and 5) identifying a dynamic obstacle from all detected obstacles, and outputting the size of a three-dimensional bounding box of the dynamic obstacle and the center position coordinates of the dynamic obstacle in the unmanned aerial vehicle coordinate system.The method provides a high-precision and low-delay solution for obstacle perception of the unmanned aerial vehicle in a weak light environment, and is suitable for different scene requirements.

The invention relates to the technical field of tunnel lighting adjustment, and discloses a high-efficiency energy-saving tunnel lighting system capable of relieving a black and white hole effect, which utilizes a dimming module outside a tunnel hole to adjust the variation amplitude of illumination intensity outside and inside the tunnel and implements a photovoltaic power generation process by means of illumination. Therefore, the visual transition process of a driver from strong light to weak light at a tunnel portal is realized, and photovoltaic power generation is comprehensively utilized to reduce energy consumption. Besides, environment information inside and outside the tunnel is obtained regularly through the tunnel environment sensing module, the environment information is transmitted to the intelligent dimming module, the intelligent dimming module intelligently analyzes the tunnel light environment under the visual comfort condition of the driver according to the environment information, and the lighting condition with the visual comfort is calculated. And the illumination intensity and the color temperature index in the tunnel are dynamically adjusted, so that the technical effect of intelligently adjusting the illumination environment in the tunnel is achieved, and the passage process of the tunnel is safer and more comfortable.

The invention relates to the technical field of atmosphere lamp control, in particular to a dynamic atmosphere lamp control method and system based on music rhythms. The method comprises the following steps: collecting a real-time audio signal stream, carrying out multi-scale wavelet decomposition and audio rhythm hierarchical structure perception, and constructing an audio rhythm feature perception map; performing spatial audio azimuth analysis based on the real-time audio signal stream, and performing adaptive dynamic atmosphere lamp parameter control based on the audio rhythm feature perception map to obtain an adaptive dynamic atmosphere lamp control strategy; according to the self-adaptive dynamic atmosphere lamp control strategy, initial atmosphere lamp control is carried out, a real-time image in the area is obtained, weak light intensity area coverage compensation optimization is carried out, and a dynamic atmosphere lamp space lighting effect optimization strategy is constructed; personalized natural physiological rhythm mining and physiological-music resonance frequency fine tuning are carried out, and user physiological-music resonance frequency fine tuning parameters are generated. According to the invention, high-response atmosphere lamp control which dynamically adapts to the light effect preference change of the user is realized.

The invention belongs to the technical field of image processing, and particularly discloses a low-light environment image adaptive enhancement method based on unmanned aerial vehicle inspection, which comprises the following steps: preprocessing a low-light RGB image shot by an unmanned aerial vehicle; the preprocessed low-light RGB image is converted to an HSV color space, and brightness and color components are obtained through separation; performing dual-channel attention mechanism processing: respectively optimizing brightness and color components by adopting a brightness branch and a color branch; wherein the brightness branch enhances the dark part contrast through a convolutional network, and the color branch suppresses a color channel with significant noise based on a channel attention mechanism; and reconstructing the optimized color and brightness components into an HSV color space, and converting the HSV color space into an RGB image format to realize brightness enhancement and color denoising. The problems of overexposure, color distortion, excessive noisy points and the like existing in the image shot by the unmanned aerial vehicle camera in the low-light environment in the prior art can be effectively solved, and the image quality under the night or weak light condition is improved.

The invention belongs to the technical field of mechanical engineering robots, and particularly relates to a railway station hidden space steel structure disease automatic inspection method comprising the following steps: S1, route autonomous planning; s2, task inspection; s3, periodically and regularly inspecting; s4, accurately and automatically avoiding obstacles; s5, intelligently identifying problems; and S6, automatically analyzing the data. Aiming at the common problems of steel member corrosion, bolt looseness, weld joint cracking, water leakage of a metal roof and the like in a hidden space of a steel structure of a high-speed rail station house roof, the intelligent inspection robot needs to have high-precision detection and identification capabilities, and can inspect and inspect the high-speed rail station house roof in a narrow space with dense rod pieces, complex pipelines and a dark or dim light environment. And comprehensive and meticulous inspection is carried out on the steel structure. Roof leakage monitoring and response speed in extreme weather can be improved, all-weather unmanned intelligent inspection is realized, and labor cost and safety risk are reduced.

The invention discloses a road target detection method and device based on bimodal feature fusion and weak light enhancement, and relates to the field of target detection. And the weak light enhancement target detection model performs enhancement and target detection on the road image under the weak light condition. And the bimodal target detection model performs target detection on the road image under the normal light condition. The weak light enhancement target detection model carries out feature extraction through a multi-scale feature attention module, and carries out image feature brightness enhancement through a brightness enhancement module. The image quality is improved, the image contrast is kept, and the target detection accuracy of the weak light image is greatly improved. The bimodal target detection model adopts a double-branch training strategy, the rich capture capability of visible light on target color and texture features under a normal illumination condition is fully exerted, the advantage of a target contour is highlighted in combination with the penetration characteristic of infrared light, and the target recognition capability under the conditions of complex background, shielding and long-distance blurring is remarkably improved.

This document describes techniques and apparatuses for computational photography under low-light conditions for an image-capture device on a mobile computing device. In aspects, described are techniques and apparatuses for an image-capture device to utilize sensor data in determining whether to enable flash photography or capture multiple images of the scene without use of a flash under low-light conditions. In other aspects, an image-capture device may utilize device data in determining whether to enable flash photography or capture multiple images of the scene without use of a flash under low-light conditions. The disclosed techniques and apparatuses may provide improved computational photography under low-light conditions for an image-capture device on a mobile computing device.

The invention provides a weak light environment image contrast improvement method for a robot welding path, and relates to the field of image processing, and the method comprises the steps: firstly improving an original compression mode based on a brightness single factor through a contrast perception compression factor; secondly, a low-light image enhancement color space based on a polar coordinate structure is introduced, and a contrast perception compression factor is combined to construct a low-light image enhancement color space with a space decoupling capability; a welding path structure response diagram is constructed by fusing the direction consistency entropy and the direction responsivity, the sensitivity to the structural characteristics with directivity and coherence such as the welding path is enhanced, and the accuracy and robustness of structure extraction are improved; and finally, through combination of the robot welding path image after color enhancement and the welding path structure response diagram, background noise interference is effectively inhibited while welding edge details are highlighted, accurate enhancement of the welding path image is realized, the welding edge details are enhanced, and the definition and the structure continuity of the image are improved.

The invention discloses a visual odometer method, system and device based on point-line features and a medium. Visual odometers are crucial to navigation and planning of autonomous robots, but weak light conditions, violent illumination changes and low texture scenes make a significant challenge to odometer estimation. Therefore, according to the visual odometer method based on the point-line features, the point-line features and deep learning are integrated, the point features and the line features are obtained through a self-supervised learning model for interest point detection and a line detection mode combining line light flow tracking and cross constraint, and the influence of complex lighting conditions is overcome. Meanwhile, the key frame is selected based on point feature counting and line feature overlapping angle constraint, and sparse matching is performed by integrating the convolutional neural network and the graph neural network, so that the accuracy and the calculation efficiency are improved.

The invention relates to the technical field of image sensor design, in particular to a multi-sampling SS-ADC circuit and module based on slope noise self-adaption. The SS-ADC circuit provided by the invention comprises a common ramp generation part DACH, a low-noise ramp generation part DACL, a comparator part COMP, a judgment logic part LOGIC, two counting parts (COUNTER1-COUNTE2), and a selection output part SELECT, and is characterized in that: the SS-ADC circuit comprises a common ramp generation part DACH, a low-noise ramp generation part DACL, a comparator part COMP, a judgment logic part LOGIC, two counting parts (COUNTER1-COUNTE2) and a selection output part SELECT. According to the invention, the low-noise ramp generation part DACL is additionally arranged to provide the quantized ramp signal RAMPL used under the condition of weak light, and different quantized ramp signals are selected to execute the CCMS technology by judging the illumination condition, so that the horizontal noise generated in the quantization process of the ramp noise under the condition of weak light is effectively reduced. The problem that horizontal noise cannot be reduced when an existing CCMS technology is applied to an ADC is solved.

The invention relates to the technical field of image recognition, in particular to a horticultural product fungal disease image recognition system and method, and the system comprises an image collection module, an image segmentation module, an anomaly detection module, a feature extraction module, a disease recognition module and a decision output module. In the prior art, the intensity of a light supplementing lamp is manually adjusted, and instantly changing illumination conditions (such as cloud layer shielding and tree shadow shaking) are difficult to deal with in an open-air plantation, so that leaf scab details are often submerged by strong light or dim light is blurred; according to the scheme, a closed-loop linkage system of ambient light perception and an annular LED is innovatively deployed, and the light supplementing intensity is reversely adjusted in a millisecond level by tracking ambient brightness in real time, so that purple gray hyphae of downy mildew in mist in the morning and anthracnose black spots under the noon burning sun present completely consistent color saturation and texture definition in imaging; and the problem of misdiagnosis caused by illumination fluctuation in the traditional method is thoroughly solved.

The invention discloses a multi-scene fire early warning and identification method and system. The method comprises the following steps: acquiring a fire monitoring video stream, and decoding the video stream into a continuous frame sequence; performing multi-scale preprocessing on the frame sequence; according to the preprocessed frame sequence, fire feature time sequence enhancement processing is carried out, and a standardized feature set D is obtained; performing multi-level feature coding and aggregation on the standardized feature set D to obtain a fire feature vector set; performing fire detection of different scenes according to the fire feature vector set V; wherein the different scenes comprise a closed space, an open space, a semi-open space, a strong light environment, a weak light environment and a normal light environment; in order to solve the problem that early warning cannot be realized due to the fact that existing fire detection is difficult to effectively recognize weak features such as spark splashing and rarefied smoke in the early stage of a fire, the method and the device specially recognize and enhance the features of flames and rarefied smoke in the early stage, fully utilize time sequence information to enhance weak signals and the like, and improve the detection precision of small targets in the early stage of the fire.

The invention discloses a self-adaptive weak light image enhancement method based on customized prompt learning. The method comprises the following steps: firstly, constructing a self-adaptive weak light image enhancement model based on customized prompt learning, and designing a dual-path processing mechanism by adopting a U-Net architecture; performing feature extraction on the input weak light image through an encoder of the encoding and decoding path to obtain multi-scale and multi-level image feature information; and further processing the image feature information extracted by the encoder through the decoder, in the processing process of the decoder, sequentially combining a plurality of different prompt features output by the prompt generation path, assisting the recovery of the image information through prompts, and finally outputting an enhanced image. According to the method, the brightness, detail representation and color rendition capability of the low-light image are remarkably improved. Experiments on a plurality of public data sets show that the method is superior to an existing method in the aspects of definition improvement, detail retention and overall visual effect.

The invention relates to the technical field of zoom glasses, and discloses a pair of intelligent zoom glasses based on a visual feedback mechanism, which comprises a glasses frame, lenses and an electronic element comprising a light sensor, an eye movement tracking sensor, a control unit and a power supply module, compared with traditional single-focal-length glasses, the glasses can cope with complex eye using scenes, for example, when different illumination rooms are switched, the focal length can be automatically adjusted, eye fatigue can be relieved, and clear vision can be guaranteed; compared with an existing glasses product, the glasses deeply integrates eye movement tracking and light sensing technologies, synergistically exerts advantages, and accurately serves vision correction optimization; the defects that most zoom glasses are not accurate in adjustment and slow in response are overcome, and accurate focusing is achieved by integrating light and eyeball focusing directions; and a protection mechanism and a self-adaptive strategy are set according to strong light and weak light, so that the visual comfort of a user is guaranteed in an all-around manner, and multiple eye using requirements are met.

A method includes detecting, based on sensor data from a sensor on a mobile device, an environmental brightness measurement, where the mobile device comprises a display screen configured to adjust display brightness based on environmental brightness. The method further includes determining, based on image data from a camera on the mobile device, an extent to which the detected environmental brightness measurement is caused by reflected light from the display screen. The method additionally includes setting a rate of exposure change for the camera based on the determined extent to which the detected environmental brightness measurement is caused by reflected light from the display screen.

A method and device is taught to improve the appearance of cellulite by combining mild exfoliation of the skin, various wavelengths of low level light, ultrasound, and a vacuum or suction. The device is intended to increase the metabolic rate of the fat cells and reduce their size, while increasing the extensibility or length of the septae. Such increased activity will promote blood flow to the affected area thereby increasing cell nutrients and removing cellular exudates while stimulating the growth of new collagen thereby alleviating the cellulite.

The invention provides an automatic illumination adjusting method and system based on seed and seedling cultivation, and relates to the technical field of illumination control and intelligent agriculture, the method comprises the following steps: firstly, collecting seedling chlorophyll fluorescence parameters to construct a light stress index, and accurately identifying a light inhibition risk; then calculating a physiologically permissible light intensity threshold adaptive to the dark response capability of the plant by combining the environment temperature and the CO2 concentration; positioning a shielded weak light area by using three-dimensional point cloud voxelization and a ray tracing technology, and calculating a shadow compensation gain coefficient; and finally, generating a control matrix in combination with a dynamic left-rotation light strategy, and driving an independent addressing LED array and a variable-angle lens to execute local precise light supplement and light vector adjustment. Through physiological feedback and spatial light field reconstruction, the problem that traditional illumination control neglects individual tolerance difference and canopy shielding is solved, photooxidation damage is effectively prevented, and the growth uniformity and the light energy utilization rate of seedling groups are remarkably improved.

The invention discloses a long-distance snapshot type spectral imaging radar system. The system comprises a light source, a projection objective lens, a photographic lens, a random mask, a grating, an SPAD and the like. Light emitted by the light source is projected to the surface of a target through the projection objective, and reflected light is sequentially processed by all the components and then detected by the SPAD. The SPAD is utilized to realize single photon detection, and the problem of spectrum detection at a long distance or under weak light and dark light is solved. Through specific light path design, a random mask plate and a grating are combined, and a light field is modulated and split. By means of a mathematical algorithm, for example, distance information is obtained based on multiple times of detection and statistics of arrival time, modulation, dispersion, integral operation and the like are carried out on a spectrum matrix, simultaneous detection of a target spectrum and the distance information is achieved, and compared with the prior art, more comprehensive and high-quality information can be obtained in a complex environment.

The invention relates to the technical field of image recognition, and discloses a YOLO v9-based lane line recognition method under strong light and weak light backgrounds, and the method comprises the steps: carrying out the scene judgment of first to-be-recognized image data based on the first to-be-recognized image data and illumination intensity data at the same moment in the automatic driving process of a vehicle, and carrying out the recognition of the first to-be-recognized image data; performing strong light suppression processing on the first to-be-recognized image data belonging to the strong light scene, and performing weak light enhancement processing on the first to-be-recognized image data belonging to the weak light scene to obtain second to-be-recognized image data; according to the illumination intensity data, a pre-trained lane line identification model is adopted to carry out lane line identification on the second to-be-identified image data to obtain a lane line identification result, the lane line identification model is constructed by adopting an improved YOLO v9 model, and a neck network of the improved YOLO v9 model is integrated with a channel attention mechanism and a space attention mechanism. According to the method, the accuracy of target detection is effectively improved.

A low-light night vision device includes a first monocular assembly. The first monocular assembly includes: a housing including a lens barrel and a front cover plate mounted to the lens barrel; a low-light objective lens group mounted on the front cover plate; an image intensifier bracket arranged at a rear end of the low-light objective lens group, and including a mounting barrel, an adjustment thread ring and an elastic member, the mounting barrel configured to allow an image intensifier to be mounted therein; an eyepiece group arranged at the rear end along the low-light optical axis. The adjustment thread ring is connected to the front cover plate and the mounting barrel, respectively, with threaded connections in opposite directions, so that the mounting barrel and the front cover plate move toward or away from each other along the low-light optical axis upon rotation of the adjustment thread ring.

The invention discloses an aerial target detection method based on dynamic attention and double-frequency feature enhancement. The method comprises the following steps: 1) extracting multi-scale features by using a backbone network in combination with a low-light feature fusion module, and enhancing detail expression in a low-light and low-contrast scene; 2) sparse modeling is carried out on deep features through a dynamic sparse attention module, key attention connection is reserved, and the global semantic ability is improved while the calculation amount is reduced; 3) inputting the deep enhanced features and the shallow features into a double-frequency feature enhancement module, and respectively modeling low-frequency background and high-frequency details to realize foreground highlighting and background suppression; according to the unmanned aerial vehicle aerial image target detection method, the problems of low illumination, complex background and small target detection are effectively solved, and the precision and robustness of unmanned aerial vehicle aerial image target detection are remarkably improved.

The invention relates to the technical field of electric connector weak light assembly vibration compensation alignment methods, particularly discloses an electric connector weak light assembly vibration compensation alignment method, and aims to solve the problems of low electric connector assembly positioning precision and high pseudo soldering rate caused by vibration in a weak light environment. The method comprises the following steps: monitoring the thermal radiation change of a vibration source in real time through an infrared thermal imaging sensor, and pre-judging a vibration acceleration vector in combination with a lightweight long-short-term memory neural network; synchronously triggering dual polarization angle imaging to respectively capture a housing reference mark and an enhanced terminal metal feature; the vibration data and the polarization image flow are fused in the field programmable gate array, a self-adaptive optical flow algorithm is adopted to resolve the displacement compensation amount, and a piezoelectric ceramic actuator is driven to carry out submicron order reverse displacement compensation; when the compensation amount exceeds a threshold value, a low-delay correction instruction is sent to the robot through an Ethernet control automation technology bus, and the steady-state error is ensured to be smaller than 0.3 micrometer by combining a strain gauge fine tuning feedback loop. According to the technical scheme, the assembling precision, efficiency and production line adaptability are remarkably improved.

The invention relates to a camera-based vehicle interior light supplement control method and system, and the method comprises the steps: determining the brightness of an environment in a vehicle through the exposure value of a camera, and carrying out the automatic adjustment of the brightness of a light supplement LED array and the brightness of a vehicle-mounted screen according to the brightness. The imaging quality and stability of the camera in the vehicle in a weak light and variable light environment are effectively improved; the method not only solves the problems that the traditional automatic exposure is insufficient in effect under low illumination and a simple light supplementing mode cannot intelligently adapt to the environment, but also considers the influence of an in-vehicle screen on imaging and brings the influence into unified control, and the camera self-exposure adjustment is assisted after the main light environment is adjusted. Therefore, a clearer and more reliable image input basis is provided for applications such as vehicle-mounted face recognition and state monitoring.

The utility model discloses an intelligent building induction signboard which comprises a main shell, a building indication board is fixed to the top end of the main shell, a weak light lamp belt is fixedly embedded in the front end of the building indication board, and a display screen and a strong light lamp belt are fixedly embedded in the front end of the main shell. A transparent shield and a smoke sensor located in the transparent shield are fixed to the front end of the main shell, a vibration sensor is installed at the rear end of the main shell, a battery, a processor, a digital signal receiver, a single-chip microcomputer and a storage are fixed to the interior of the main shell, and a power connection port is formed in one side of the main shell. The signal input end of the processor is electrically connected with the smoke sensor and the vibration sensor, the output end of the processor is electrically connected with the digital signal receiver and the accent light strip, the processor is electrically connected with a memory for storing building information of the building escape channel, and the processor is used for calling the memory information to the display screen for displaying according to disaster signals. According to the emergency escape lamp, high-brightness illumination during disasters is achieved, the reliable auxiliary escape function is achieved, and the functions are diversified.