3020 results about "Illuminance" patented technology

The invention discloses a visible light and infrared image fusion method based on cross-modal dynamic collaboration. The method comprises the following steps: respectively extracting texture detail features of a visible light image and thermal radiation features of an infrared image through a visible light encoder and an infrared encoder; spatial alignment and channel complementarity optimization of cross-modal features are realized by using a heterogeneous attention collaboration module; and performing layered fusion on deep semantics and shallow detail features through a dynamic gating multi-scale decoder to generate a high-resolution fusion image. According to the method, the problems of feature dislocation, detail loss and unreasonable fusion weight distribution caused by modal difference in the prior art are solved, the detail fidelity, the thermal target saliency and the complex scene adaptability of the fusion image can be remarkably improved, and a high-robustness fusion result is provided for low-illumination environment perception and multi-modal target recognition.

The invention provides an improved YOLOv11s safety helmet wearing detection model and an optimization method thereof, and relates to the technical field of computer vision target detection. According to the improved YOLOv11s safety helmet wearing detection model and the optimization method thereof, the improved YOLOv11s safety helmet wearing detection model comprises the following modules: a multi-modal fusion module, a space-time analysis module, a domain adaptation module, a topological optimization module and a dynamic architecture module; and the multi-modal fusion module is used for realizing feature decoupling by adopting channel separation convolution based on input RGB and near-infrared images, fusing visible light and thermal radiation features through a dynamic weight distribution algorithm, implementing affine transformation alignment on multi-scale features by utilizing a spatial transformation network, and generating a multi-modal feature graph. Through fusion of visible light and near infrared spectrum features and implementation of dynamic weight distribution, complementarity of target texture and thermal radiation features under a complex illumination condition is enhanced, and the problem of feature distortion of single-mode data in a strong backlight or low-illumination scene is solved.

The embodiment of the invention provides an intelligent illumination automatic adjustment method and device based on illumination intensity and personnel detection, and reliability evaluation and abnormity correction are carried out on data by synchronously collecting environment illumination intensity and personnel activity data. An illumination activity association model is innovatively constructed, and feature fusion is realized by using a fuzzy logic algorithm. The system establishes a complete evaluation index system including illumination uniformity, a color rendering index and a glare value, and performs constraint optimization based on an illumination scene adaptation model. Through introducing a dynamic compensation mechanism of a personnel detection probability value and a time period priority factor, accurate illumination adjustment control is realized. The method breaks through the limitation of dimming in a traditional fixed mode, and provides a comprehensive technical solution for an intelligent lighting system.

The invention discloses a wireless control method for LED illumination, a dimming device, a medium and a product, and relates to the technical field of electric illumination, and the method comprises the steps: obtaining tunnel illumination control parameters; dividing the tunnel entrance area into an entrance section, a buffer section and a basic section according to the tunnel parameters, and determining a plurality of illumination control subareas; calculating the brightness adaptation time of a vehicle driver based on the external brightness value and the vehicle passing speed, and generating an illumination gradient curve from the entrance section to the buffer section and the basic section; according to the illumination gradient curve, determining a target illumination value of each illumination control zone; converting the target illumination value into an LED driving parameter, and issuing the LED driving parameter to an LED driving circuit of each illumination control subarea; and collecting actual illumination data of each illumination control subarea, and regenerating an illumination gradient curve when the actual illumination data exceeds a preset target range or the external brightness value changes. By implementing the method, tunnel illumination can be adjusted based on illumination changes when the weather changes drastically, and driving safety is guaranteed.

The invention discloses a low-light image enhancement method based on multi-scale frequency domain guidance and a double-branch attention mechanism. According to the method, a low-light image and a normal image corresponding to the low-light image serve as input, and illumination mapping and illumination features are extracted through a layer decomposition network; a reflection image mapping relation is learned in combination with a damage recovery network, and a preliminary enhanced image is generated; a learnable Fourier transform module is provided to realize frequency domain feature extraction, and low-frequency illumination information and high-frequency details are effectively separated; a dynamic double-branch attention mechanism is put forward, spatial domain and frequency domain features are fused, and collaborative modeling of structure and illumination perception is achieved; a multi-scale feature module is proposed to combine channel rearrangement and residual fusion to optimize feature expression; a U-Net framework is combined with a double-branch large kernel activation attention mechanism to complete image reconstruction, cross-scale feature fusion and detail recovery are achieved, and finally an enhanced image is output. According to the method, the problems of uneven illumination, detail loss, noise interference and the like in the low-light image can be effectively solved.

The invention provides a cooperative control method and system for a distributed intelligent lamp network, and the method comprises the steps: obtaining the real-time operation state data of each lamp in the intelligent lamp network, recognizing the failure position of the lamp, generating a fault node coordinate according to the failure position of the lamp, and obtaining the distribution information of the failure lamp; extracting spatial position data and current illumination parameters of surrounding lamps according to the distribution information of the failed lamps, calculating an illumination dead angle range and a local illumination decline region by adopting a geometric algorithm, and determining an affected light environment region range; acquiring communication time delay and response rate between the lamps, and if the communication time delay exceeds a preset time delay threshold, reducing the adjustment frequency and reallocating the compensation task to obtain an optimized cooperative compensation scheme; and extracting residual range data of the illumination dead angle according to the adjusted illumination distribution result, carrying out secondary adjustment on the illumination angle and the luminous intensity through an iterative algorithm, and determining a light environment continuity parameter after the illumination dead angle is eliminated.

The invention relates to the technical field of lighting energy efficiency management, in particular to a smart city lighting energy efficiency management system based on the Internet of Things, and adopts the technical scheme that the smart city lighting energy efficiency management system comprises a data acquisition and analysis module, an intelligent control module, a fault detection and early warning module, an energy management module and a report and statistical analysis module, environment data and traffic flow data are acquired in real time through an illuminance sensor, a temperature sensor, a humidity sensor, a pedestrian flow sensor and a traffic flow sensor which are deployed in each region of a city. According to the invention, through multi-sensor data acquisition and a complex algorithm, the illumination demand is accurately predicted, and personalized brightness adjustment and energy saving are realized; stable operation of the system is ensured through efficient fault detection and early warning; energy management is optimized, and efficient utilization of energy is realized in combination with a genetic algorithm and LSTM prediction; and the report and statistical analysis module provides visual display and decision support, so that efficient utilization of energy and optimization of the lighting effect are realized.

The invention belongs to the technical field of digital image processing, and particularly discloses a low-illumination image enhancement method for simulating a foggy day degradation mechanism, and the method comprises the following steps: generating a virtual haze image; estimating atmospheric light searched by combining decreasing cutting with a genetic algorithm; coarse transmissivity calculation based on dark channel prior; refining the transmissivity graph; carrying out defogging and low-illumination enhancement on the virtual haze image; and brightness and contrast self-adaptive compensation. According to the method, the precision and robustness of atmospheric light value estimation are improved, a transmissivity graph which better conforms to a scene can be obtained, and the image restoration effect is improved; the method has remarkable advantages in the aspects of reducing artifacts, enhancing image details, denoising, keeping structural information and restoring details.

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 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 discloses a general lens intelligent core adjustment method and system based on a lens library and optical simulation, and belongs to the field of optical lens core adjustment. Adding different simulation lenses into a simulation light path system, taking a clear extended image as input in each view field, calculating each view field point spread function matrix of the simulation lenses under different misalignment amounts, and taking the misalignment amounts as labels; obtaining an illuminance distribution matrix and a distortion distribution matrix corresponding to each misalignment amount at the same time; a series of ideal degraded images under different misalignment amounts are calculated, and simulation degraded images are obtained through optimization of the illumination distribution matrix and the distortion distribution matrix; the simulation degraded image and the misalignment label are used for training a universal lens intelligent core adjustment model, and intelligent core adjustment of lenses of the same optical structure can be achieved after fine adjustment. According to the method, aberration characteristic priori of a large number of simulation lenses is utilized to pre-train the core adjustment model, generalization of the model to different lenses is improved, and the purpose of performing quick core adjustment on any lens by only using one model is achieved.

The invention discloses a container number tallying identification method, and relates to the technical field of container intelligent tallying. According to the technical key points, the method comprises the following steps: deploying a multi-modal sensing device, carrying out initial calibration, and pre-training an improved YOLOv7-tiny target detection model, a transformer-based cGAN character segmentation model and a lightweight CNN-transformer-capsule network integrated hybrid recognition model; through cooperative collection of multi-modal data of the multi-modal sensing device, the system can effectively integrate texture information of RGB images, a space structure of depth images and three-dimensional coordinates of point cloud data, the problem that traditional single visual recognition is affected by illumination, shielding and the like is solved, a self-calibration mechanism corrects space-time deviation of a sensor in real time, and the accuracy of recognition is improved. The system can still accurately extract container number characteristics in complex scenes such as strong light reflection, low illumination at night, character abrasion and the like.

The invention provides a low-light large dynamic image enhancement method based on dynamic weight and pyramid fusion. The low-light large dynamic image enhancement method is suitable for complex illumination imaging in security monitoring. According to the method, the global weight and the detail weight of an image are obtained according to the global average brightness, the local brightness and the gradient of an image sequence, and the total weight is obtained through weighting. And the halo phenomenon easily occurring in the fusion process is reduced through guide filtering. And combining Gaussian pyramid and ratio pyramid decomposition and weighting to obtain a final fusion image. According to the method, the problems of detail loss and limited dynamic range in the low-light scene are effectively solved. Experiments show that the method is superior to the prior art in information entropy, average gradient, edge strength, spatial frequency and other key indexes. The technology has an all-time adaptive capability, is suitable for the fields of intelligent transportation, urban security and protection, low-illumination monitoring and the like, and has a wide market prospect.

The invention discloses a low-illumination image enhancement method based on a Retinex theory and guided by color priori, a low-illumination image enhancement network model based on the Retinex theory and guided by the color priori is constructed for low-illumination image enhancement, the model is based on the Retinex theory, the color priori is generated through a color priori generation module, and the low-illumination image enhancement network model is used for enhancing the low-illumination image. The color priori and the original image are spliced to form a combined image to guide low-illumination image enhancement, the pixel value of the image is improved through an image illumination enhancement network, and an illumination image and a feature image are generated. The illumination image is input into a denoising network to generate a denoising component, the denoising network is provided with an attention module guided by illumination, and the attention module introduces a multi-scale double-gating feed-forward network, so that the denoising network realizes image denoising on different scales, and key content of the image is highlighted. According to the low-illumination image enhancement method provided by the invention, the problems that part of the enhanced region in the enhanced image deviates from the original color and the noise is amplified in the process of enhancing the low-illumination image are solved.

The invention relates to the technical field of virtual modeling, in particular to a lamp arrangement method and system based on virtual modeling and a medium. The method comprises the following steps: obtaining dynamic monitoring data of a target space, wherein the dynamic monitoring data comprises real-time illumination intensity data, people flow density data and ambient light change data; performing prediction modeling on the illumination demand of the target space according to the dynamic monitoring data to obtain illumination demand prediction data including expected illumination requirements and light distribution characteristics in different time periods; the adaptive light propagation grid is constructed in the digital twinborn space based on the illumination demand prediction data, grid division data are obtained, and the adaptive light propagation grid has high grid density in a region with severe illumination change and low grid density in a region with smooth illumination change. According to the method, comprehensive illumination analysis and evaluation can be carried out in the initial stage of design, and blindness and low efficiency in a traditional design method are avoided.

The invention relates to the technical field of lighting, in particular to a driving power supply control circuit, a control method and a lighting application method. Comprising the steps that an acquisition module acquires spectral data and temperature data of a current environment; the receiving module receives the target color rendering index, the target color temperature and the target illumination; the control module is connected with the acquisition module and the receiving module, and is used for calculating a PWM duty ratio needing to be compensated according to the temperature data, obtaining a target PWM duty ratio based on the current color rendering index Ra, the current correlated color temperature CCT, the current illumination and the control parameters, and outputting a PWM signal based on the target PWM duty ratio and the PWM duty ratio needing to be compensated; and independent and stable constant-current signals are finally output by using the voltage balancing circuit and the driving module. The problems that an existing LED driving power source is single in function and lacks self-adaptive control capacity and monochromatic light trailing are solved. The LED powder-free lamp can continuously and stably operate, and meanwhile, high color rendering index and dynamic light color balance are achieved.

The invention relates to a three-dimensional low-illumination scene brightening method based on 3D Gaussian sputtering, computer equipment and a program product, the three-dimensional low-illumination scene brightening method comprises the following steps: a plurality of fulcrums are arranged in a three-dimensional space, the attributes of the fulcrums comprise fulcrum position attributes and Gaussian point positioning attributes used for determining the positions of Gaussian points associated with the fulcrums, and the attributes of the fulcrums comprise the fulcrum position attributes and the Gaussian point positioning attributes used for determining the positions of the Gaussian points associated with the fulcrums; the attribute determining module is used for determining appearance attributes and geometric attributes of Gaussian point features associated with fulcrums; extracting reflection components and illumination components participating in rendering according to the appearance attributes; optimizing a fulcrum position attribute, a Gaussian point positioning attribute, an appearance attribute and a geometric attribute according to the difference between the 2D image and the 3D Gaussian rendering image under the corresponding view angle; extracting a gamma value from the appearance attribute by using a multi-layer perceptron to obtain an enhanced illumination component; and multiplying the reflection component by the enhanced illumination component to obtain a Gaussian point color, obtaining brightness enhanced images under different visual angles through volume rendering, and obtaining a brightness enhancement loss optimization multilayer perceptron according to the brightness enhanced images.

The invention relates to an indoor lighting rate simulation system for indoor design. The system comprises a dynamic meteorological simulation module which generates a dynamically changing sky model based on real-time meteorological data; the building attribute analysis module analyzes building optical attribute parameters by using the building information model data and the hyperspectral imaging data; the light ray tracing processing module performs light ray tracing based on the dynamic sky model and the building optical attribute parameters to obtain indoor glare distribution data; a reverse parameter optimization module generates window form parameters and sunshade device layout parameters according to the target illumination interval and the indoor glare distribution data; and the real-time visual verification module carries out visual rendering on the window form parameters and the sunshade device layout parameters to obtain an updated lighting rate simulation result for verifying the lighting effect of the design scheme. According to the system, through dynamic meteorological fusion, non-uniform material modeling and real-time optimization feedback, the precision and adaptability of indoor lighting simulation can be improved.

The invention provides a garage light energy-saving management system and device, and the method comprises the steps: obtaining the environment illuminance and traffic flow parameters of a garage based on a sensor network, and building a garage light energy-saving control model according to the environment illuminance and traffic flow parameters; obtaining a garage light energy-saving control strategy based on the garage light energy-saving control model parameters; generating an expected lighting strategy of the garage lighting system through a scene control and induction control algorithm, wherein the expected lighting strategy is dynamically adjusted along with the traffic flow parameters; constructing an intelligent control model of the garage lighting system; and receiving an illumination instruction of the central control system, updating the illumination instruction based on the intelligent control model, and performing illumination control based on the updated illumination instruction to realize optimal management of light brightness and energy consumption. Energy consumption can be reduced, and lighting efficiency can be improved.

The invention belongs to the field of illumination light control, and relates to a dynamic tunnel illumination optimization method based on driver visual adaptation characteristics, which comprises the following steps of: establishing a relation model between a driver pupil area and ambient illuminance in a tunnel, and calculating a pupil area change rate according to the driver pupil area; establishing a corresponding relationship among the average glancing speed, the pupil area change rate and the ambient light in the tunnel according to the historical data; constructing a calculation model of the total average illumination of the lamps on the road surface in the tunnel based on the natural illumination outside the tunnel and the average horizontal illumination of the lamps on the road surface; an energy consumption and light pollution objective function and a driver psychological load objective function are established based on parameters such as total average illumination of lamps on a road surface in a tunnel and the length from an entrance or an exit, and multi-source data such as illumination, brightness, lamp power, pupil change rate and glancing speed are deeply fused, so that human-vehicle-environment multi-dimensional collaborative optimization is realized. The driving safety and the visual comfort of a driver are improved, and meanwhile, the energy consumption is considered.

The invention provides an infrared and visible light image enhancement fusion method and system for a low-illumination scene, and the method comprises the steps: obtaining an infrared image and a visible light image, and carrying out the preprocessing of the infrared image and the visible light image; inputting the brightness image and the illumination prior image into an encoder, and performing single-mode reconstruction pre-training through a minimization reconstruction loss function; performing deep feature extraction on the dual-channel visible light image and the normalized infrared image by using a pre-trained encoder, performing bidirectional feature guidance and fusion in a fusion module in a manner of exchanging query vectors by using a cross-modal cross attention mechanism, and reconstructing the fused features by using a pre-trained decoder; according to the method, low-illumination image enhancement and cross-modal feature fusion are integrated into a unified framework, brightness improvement, detail reservation and modal consistency synchronous optimization are realized through end-to-end joint modeling, and information loss caused by enhancement and fusion process separation is avoided.

The invention relates to a low-illumination image enhancement method based on central difference convolution, and belongs to the field of image processing. The method comprises the following steps: firstly, dividing a brightness component of an image in an HSV color space into a bright region and a dark region through a binary mask, and calculating the average brightness of each region; performing exposure of different degrees on the brightness component of the image according to the average brightness of different areas through an exposure method based on a binary mask; fusing the brightness components with different exposure degrees by the central difference convolutional neural network under the guidance of the fusion loss function; and finally, the amplified noise is suppressed and texture details in the image are enhanced under the guidance of the content loss function through the other central difference convolutional neural network. According to the method, noise can be suppressed, texture details can be enhanced, and the image definition can be improved; moreover, the method has a better enhancement effect on images under a non-uniform illumination condition and an extremely low illumination condition.

The invention relates to the technical field of intelligent lighting, and discloses a high-luminous-efficiency LED lamp module lighting system which comprises a high-luminous-efficiency LED lamp module used for emitting a light source with preset brightness and color temperature. The constant illumination sensor is used for detecting ambient light in real time and automatically adjusting the brightness of the LED lamp module through a negative feedback algorithm; the existence induction sensor is used for detecting the existence of personnel and triggering the lighting state of the LED lamp module; and the dimming and color adjusting module is electrically connected with the high-luminous-efficiency LED lamp module and is used for adjusting the brightness and the color temperature of the light source according to the output signal of the constant illumination sensor. The purpose of saving energy is achieved through intelligent dimming, constant illumination and existence induction technologies, and energy consumption is remarkably reduced; high-quality lighting which is free of stroboflash, low in glare, healthy and capable of protecting eyes is provided, and the comfort level of a user is improved; the wireless ad hoc network technology is adopted, installation and maintenance are easy and convenient, high expansibility and stability are achieved, and remote management and upgrading are supported.

The invention relates to the technical field of liquid crystal display, in particular to a liquid crystal display driving control method and system. Comprising the following steps: SS01, multi-modal sensing and state triggering: fusing a user distance, a fixation point coordinate and ambient light illumination in real time by using an integrated sensor, and triggering an eye protection mode or a self-adaptive sleep mechanism according to the user distance, the fixation point coordinate and the ambient light illumination, SS02, image partitioning and strategy generation: dividing a screen into N * M partitions, and SS03, distinguishing a dynamic region from a static region according to the brightness histogram and the motion vector of each partition, and outputting differentiated refresh rates and backlight strategies, and SS03, performing big data learning and model optimization, and collecting user behaviors. The system has the advantages that the infrared proximity sensor, the eyeball tracking sensor, the distance sensor and the ambient light sensor are integrated through the multi-mode sensing and state triggering module, multi-dimensional data such as user distance, fixation point coordinates and ambient light illuminance are fused in real time, and active intelligent sensing of the user state and the environment is achieved.

The invention discloses an image denoising and self-adaptive enhancement method based on pixel discretization and illumination guidance, and relates to the technical field of image processing, and the method comprises the steps: carrying out the down-sampling and noise reduction of an input image, and obtaining a preliminarily denoised image through the deconvolution operation, fuzzy pixel discretization and residual serialization; estimating an illumination component from the preliminarily denoised image by using an illumination estimation network; calculating a reflection image based on the estimated illumination component and the preliminarily denoised image according to an image enhancement theory; based on a dynamic splicing mechanism, splicing the reflection image and the illumination component in the channel dimension to obtain a spliced image; and with keeping of image details and brightness information as a criterion, denoising and enhancing are carried out on the spliced image through a reflection denoising network, and a denoised and enhanced image is output. According to the invention, high-quality recovery of low-illumination images is realized, and the intelligent level of an electric power inspection system is improved.

The invention provides an intelligent blind person navigation system, and relates to the technical field of intelligent wearable blind person navigation, the intelligent blind person navigation system comprises a multi-mode sensing unit, an A I decision center, a bidirectional feedback module and an emergency response system.According to the intelligent blind person navigation system, through a three-layer sensing architecture of a dual-light fusion camera, a laser radar and an ultrasonic array, 94% of obstacle detection rate is still kept under 5lux low illumination, and the blind person navigation efficiency is improved. And the effective detection distance reaches 8 meters. The semantic segmentation algorithm enables the detection rate of the suspended obstacle to rise from 12% to 92%, and the recognition error of the glass door in the underground garage scene is only 2.1 cm. And a multi-sensor space-time alignment technology is adopted, so that the false alarm rate in a complex scene is reduced from 25% to 3.2%, and all-weather and full-height accurate sensing of obstacles is realized. A biological characteristic + environmental risk multi-stage triggering mechanism is adopted, automatic alarming and shared positioning are performed within 2.1 seconds when a user falls down, and the success rate of rescue for falling down of the underground garage is increased from 62% to 100%. An environment video with positioning is automatically recorded without operation timeout, and a'prevention-early warning-rescue 'closed-loop safety net is constructed.

The invention relates to a light sensation self-adaption and white balance cooperative adjustment method and device for an LED display screen, and the method comprises the steps: collecting the illumination value of current ambient light and the spectral distribution of the ambient light, and calculating the color temperature value of the ambient light; according to the color temperature value, inquiring a pre-stored color temperature-gain parameter corresponding relation library to determine a corresponding white balance gain parameter; according to the illumination value, a backlight brightness adjustment amount is calculated through a pre-constructed logarithm compensation model; associating and binding the white balance gain parameter and the backlight brightness adjustment amount, and outputting the white balance gain parameter and the backlight brightness adjustment amount to a pulse width modulation signal generation unit and a digital-to-analog converter through a microcontroller; and outputting the white balance gain parameter to a digital-to-analog converter, outputting the backlight brightness adjustment amount to a pulse width modulation signal generation unit, generating a synchronous adjustment signal to drive an LED display unit, and carrying out cooperative adjustment of backlight brightness and white balance so as to achieve the purpose of unified regulation and control of visual comfort and color rendition authenticity.

The invention relates to the technical field of intelligent maintenance of tunnel electromechanical facilities, in particular to a tunnel lamp recognition and spatial positioning method based on binocular vision, which comprises the following steps: synchronously acquiring tunnel environment images through left and right cameras subjected to polar horizontal correction, inputting the images into a lightweight tunnel lamp detection model, and detecting an area image containing a lamp bounding box; extracting feature points in the bounding box by adopting a multi-scale adaptive FAST feature extraction algorithm, describing the feature points by rotating an invariant binary descriptor, and generating a disparity map in combination with an adaptive Hamming distance matching algorithm; calculating three-dimensional space coordinates of the lamp according to the disparity map by using a binocular camera triangulation principle and combining camera parameters; when the ambient illumination is lower than a threshold value, a multispectral image fusion module is started to enhance the feature matching robustness; and when continuous matching fails, visible light communication assisted positioning is started. According to the invention, the problem of low positioning precision of the lamp in a complex tunnel environment is solved, and the reliability and efficiency of identification and positioning are improved.

The invention relates to the technical field of illumination design, and discloses a transformer substation indoor illumination design method, system and device and a storage medium, and the method comprises the steps: obtaining real-time data, spatial feature data and meteorological data of a transformer substation, inputting the data into an illumination prediction model, and obtaining the illumination demand value of each region of the transformer substation; inputting the illumination demand value and the environmental parameters into a lamp selection model to obtain a lamp layout initial scheme; establishing a three-dimensional simulation model of the transformer substation according to the transformer substation design drawing and the lamp layout initial scheme, and performing iterative simulation to obtain a lamp layout optimization scheme; performing clustering analysis on the lamp layout optimization scheme by adopting a clustering algorithm to obtain a plurality of lamp groups, and setting an independent power distribution loop for each lamp group; and generating a control strategy corresponding to each lamp group according to the illumination demand value. According to the invention, the design precision, the energy efficiency level and the operation and maintenance convenience of the lighting system are remarkably improved by fusing dynamic prediction, multi-objective optimization and intelligent control technologies.

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.