148 results about "Low level light" patented technology

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 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 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 12M large-wide-angle security monitoring lens which sequentially comprises a diaphragm and first to ninth lenses from an object side to an image side, the first and second lenses have negative diopters, the third, fourth, sixth to ninth lenses have positive diopters, and the fifth lens has negative diopter. The aspheric lens is matched to correct aberration and suppress distortion. The working wave band of the lens covers 435-1000 nm, the F number is about 1.3, the maximum imaging circle diameter is about 16.8 mm, the TTL does not exceed about 22 mm, the relative illumination can reach about 39%, the main light angle is about 23 degrees, the lens is adaptive to a 12M large-size sensor, full-view-field high resolution, low distortion and clear imaging under weak light / infrared supplementary light are achieved, and the lens is suitable for security and protection monitoring equipment.

The invention provides a port container loading and unloading positioning method, system and equipment and a medium, and belongs to the technical field of port automation and machine vision positioning. The method comprises the following steps: acquiring a container surface image and synchronous sling motion data; the jitter amplitude is evaluated, the image is combined to analyze the illumination condition, and the multispectral image is adaptively selected or fused to generate an anti-interference image; angular point coordinates are extracted, the angular point coordinates and motion data are input into a vision-inertia tight coupling optimization model together for state estimation, and the real-time pose is solved; driving a lifting appliance-load dynamic model by using the motion data, and predicting pose deviation generated by inertia and swinging in the future; and the feedback control quantity based on the real-time pose and the feed-forward compensation quantity based on the prediction deviation are fused, and an accurate control instruction for driving the lifting appliance is generated. The problems of image quality degradation and feature loss caused by strong light, weak light and sling shaking are solved, and high-precision and high-robustness container positioning and alignment in a complex dynamic environment are achieved.

The application discloses a gas heater control system capable of automatically adjusting the size of a flame, comprising a main controller, an infrared sensing device, a gas control valve, a burner and an ambient temperature sensor and the like. The infrared sensing device senses the presence state, distance and height information of a human body, and the ambient temperature sensor monitors the ambient temperature; the main controller generates a multi-stage or continuous gas flow instruction based on the above information, controls the gas control valve to adjust the size of the flame, and can also adjust the total flow according to the number of human bodies. The system is equipped with a lifting device, which can automatically adjust the height of the burner according to the sitting or standing posture of the human body; a wind speed sensor is arranged, and when the flame is extinguished, the wind speed is combined to judge whether to delay ignition; multiple protections such as a tilt switch, an audible and visual alarm and the like are arranged, and an auxiliary illuminating lamp is started to enhance the sensing performance in weak light. The system realizes intelligent and accurate temperature control, improves the safety and scene adaptability, is energy-saving and convenient.

The utility model provides septic tank identity card code acquisition and identification equipment, which comprises an equipment main body, a display screen is arranged on the front side of the equipment main body, a scanning assembly is arranged on the rear side of the equipment main body, the scanning assembly comprises a camera module, an auxiliary front sight module and a light supplementing module, and a light shielding plate is detachably arranged on the rear side of the equipment main body. According to the utility model, shielding can be flexibly adjusted according to illumination conditions, the problem of screen light reflection caused by direct sunlight is effectively avoided, the operation time cost in a strong light environment is greatly reduced, and the continuity of a code scanning process is ensured; meanwhile, through a built-in light supplementing module and an auxiliary front sight module, a precise recognition system under a low light condition is constructed, the problem that a code scanning area is difficult to position under a weak light environment is solved, low-efficiency operation of traditional equipment depending on experience exploration is avoided, code scanning time consumption is shortened, and operation efficiency under a complex light environment is remarkably improved.

The invention discloses a TSTFP-Net-based semantic segmentation method for a low-illumination superposition rain image, and the method aims at an image segmentation scene shot in a weak light and rainy environment, and solves a problem that the semantic segmentation precision is reduced because of insufficient illumination, raindrop interference and a significant distribution difference between a source domain and a target domain. The method comprises the following steps: 1) processing an existing data set; 2) designing a semantic segmentation network based on low illumination and rain superposition of TSTFP-Net; 3) training the RIPLM network; 4) training a trunk segmentation network; 5) training a space domain and frequency domain double-teacher network; the method comprises the following steps of (1) training an FSFM module, (2) training a comparison learning module, (3) training a domain discriminator network, and (4) testing a semantic segmentation network with low illumination and rain overlay. The method can still accurately identify the contour and category of an object in complex weather, improve the domain generalization performance, reduce the domain gap, and further improve the precision of semantic segmentation in the low illumination and rain overlay environment.

The application relates to the technical field of safety monitoring in an industrial environment, and discloses an image processing method and system for an industrial environment, which trains an input image enhancement network framework by taking a weak-light image in a video frame as the input image; in the training, the weak-light image is input into an initialization module to obtain initial reflectivity and initial illumination output by the initialization module; the initial reflectivity, the initial illumination and normal illumination are input into an optimization module to iteratively refine a reflection layer and an illumination layer, so that target illumination is obtained; the target illumination is input into an image enhancement module to obtain an output enhanced image; and an image frame of the industrial environment collected in real time is input into the image enhancement model to obtain an enhanced image. The method can solve the problem that, due to poor lighting conditions and excessively high dust and smoke concentration in some industrial environments, the real-time monitoring technology causes low video image quality of a monitoring video, thereby affecting the final recognition result.

The invention provides a self-powered wireless sensor node energy efficiency control method and system for an indoor weak light scene, and belongs to the technical field of self-powered wireless sensors of the Internet of Things. Comprising the following steps: constructing an energy collection and energy perception calculation model which comprises an energy collection module, an energy perception calculation module, an energy prediction module and an energy management module; the energy sensing calculation module dynamically adjusts the capacitor capacity by using a temperature correction model in the process of calculating available energy according to the super-capacitor voltage; the energy prediction module performs prediction by adopting a mixed entropy weight rolling adaptive energy prediction algorithm based on low energy density and high-frequency fluctuation characteristics of an indoor weak light scene, and dynamically adjusts a prediction weight according to an energy fluctuation condition by distinguishing situations, controlling dormancy and quantifying energy fluctuation. Balancing the indoor energy prediction precision and the energy consumption in the energy collection process; and the energy management module performs energy efficiency control based on the model, calculates an energy prediction value of the next time slot and performs working mode control of the node.

The application discloses a pedestrian target recognition method in a weak light environment based on multi-modal data fusion, and relates to the technical field of intelligent vehicle automatic driving, to solve the problem that the target detection effect of the prior art is poor in a weak light environment. The method comprises the following steps: a traffic scene database containing pedestrians and vehicles is constructed, the database comprising infrared images and visible light images; the infrared images and the visible light images are subjected to feature registration and space-time alignment, and are divided into a training set, a verification set and a test set in proportion; a double-branch target detection network based on multi-modal data fusion is provided, the double-branch target detection network is trained with the training set as input, a Dark RGBT -YOLO network model suitable for a weak light environment is obtained; the test set is input into the Dark RGBT -YOLO network model to detect targets and obtain detection results; and position risk scores and distance risk scores of each pedestrian target in the images are analyzed according to the detection results, the position risk scores and the distance risk scores are added together to obtain a total risk assessment result.

The invention discloses a photovoltaic panel tracking system, which relates to the technical field of photovoltaic power generation, and is characterized in that illumination intensity difference is sensed in real time through a four-quadrant photosensitive element so as to judge the real-time azimuth of the sun and set a photoelectric tracking mode, and then in specific tracking, when the illumination intensity is higher than or equal to a preset threshold value, the photoelectric tracking mode is started, and the photovoltaic panel tracking system is started. A photovoltaic panel is driven to be aligned with the sun in real time, when the illumination intensity is lower than a preset threshold value, a time control tracking mode based on preset sun trajectory characterization is switched, in the process, a photoelectric tracking mode can be started when cloudy, cloudy and rainy weather changes suddenly, and the time control tracking mode can be started when light intensity distribution is uneven due to weak light or local cloud layer shielding. According to the invention, the mode switching is dynamically tracked under different environmental conditions, so that all-weather and high-precision tracking of the sun trajectory is realized, finally, sufficient energy conversion is carried out according to the self-adaptive light intensity change of the environmental conditions, and the energy conversion efficiency of the photovoltaic panel is greatly improved.

The application provides a roadside weak light target detection method and device based on multi-branch feature fusion, and relates to the technical field of target detection and intelligent transportation. Real-time road image data collected by a roadside monitoring device is acquired; and the real-time road image data is input into a pre-trained roadside weak light target detection model DBRW-DETR model to perform detection reasoning and output a result. The DBRW-DETR model integrates a DBB multi-branch structure and a wavelet pooling layer in a backbone network to retain texture; a wavelet inverse pooling layer and a wavelet pooling layer are used for sampling in an encoder, and a Ret-RepC3 module integrating a space perception mechanism is combined to strengthen feature fusion. The application can solve the problem of feature loss under low illumination, retain high-frequency edges while reducing dimensions, significantly improve the accuracy and robustness of target detection under complex illumination, and meet the real-time requirement of roadside perception.

The invention belongs to the field of photovoltaic power generation, and discloses a photovoltaic panel dust deposition thickness monitoring method and system based on multi-mode perception, and the method comprises the steps: employing an illumination intensity threshold discrimination model to dynamically generate a thickness weight according to ambient light, improving the gray component confidence coefficient in weak light, and emphasizing the weight data in strong light; the gray scale-thickness mapping model compensates illumination fluctuation errors by establishing stable correlation between gray scales and ash deposition; and the weight-thickness conversion model provides a thickness reference value of a physical layer. After the three-modal data is subjected to weight adaptive fusion, the failure risk of a single sensor in extreme weather or uneven dust deposition is avoided, and the macroscopic stability of the weight data and the spatial resolution of the gray data are utilized to form complementation; high-precision optical equipment is replaced by a low-cost sensor combination, so that the hardware overhead is remarkably reduced while the precision is ensured; and finally, ash deposition monitoring with high all-weather environment adaptability and excellent anti-interference capability is realized, and intelligent operation and maintenance support with high cost performance is provided for a distributed power station.

The application relates to long-afterglow light-emitting concrete and a preparation method thereof, and belongs to the technical field of building engineering materials. The long-afterglow light-emitting concrete is prepared from the following components: cementitious material 420-485 parts by weight, water reducing agent 8-10 parts by weight, long-afterglow light-emitting material 5-10 parts by weight, admixture 5-7.5 parts by weight, and water 50-80 parts by weight. The long-afterglow light-emitting material is combined with high-performance concrete, the light penetration and excellent light-emitting performance of the long-afterglow light-emitting material are utilized, and high-performance long-afterglow light-emitting concrete with high strength and long afterglow time is obtained, which can be used in the fields of emergency passage indication, weak light illumination, building decoration and art crafts.

The application discloses a kind of based on double light activation layer's super-sensitivity phototransistor.The phototransistor described in the application is sequentially composed of gate electrode (Si), dielectric layer (HfO2), photoactive ion layer (PbI2), channel (WSe2) and source-drain electrode (Au) from bottom to top.The phototransistor described in the application exhibits extremely high sensitivity advantage: on the one hand, it meets the test demand in extremely weak light environment, and the dark field light intensity detected by it is lower than that of commercial phototransistor by 4 orders of magnitude;On the other hand, its responsivity is higher than that of commercial product by 4 orders of magnitude.PbI2, a photoactive ion layer, is introduced into the traditional phototransistor structure based on traditional two-dimensional material for the first time, which greatly improves the sensitivity of the device and provides a new idea for the development of extremely dark field detection technology.

This invention proposes a traffic sign light-sensing algorithm, device, and system based on an improved HSV (High-Speed ​​Vector Characteristic). The light-sensing algorithm includes: using adaptive HSV threshold adjustment combining global brightness statistics and local block segmentation to generate a dynamic HSV threshold suitable for the current scene; and using this dynamic threshold to perform color segmentation on the image to obtain the target color region. Compared to the inherent HSV threshold, or solely global brightness statistics, or solely local block adaptive threshold correction, this overcomes the interference of different reflectivities and absorbances of objects in the environment, which cause interference in color recognition of different regions of the image under complex lighting conditions such as strong light, weak light, backlight, and local shadows. This significantly improves the robustness and accuracy of traffic sign recognition in dynamic, unstructured scenes.

The application discloses a readout circuit for improving dynamic range based on a composite medium grid photosensitive detector and belongs to the field of integrated circuits. An overflow electron discharge circuit is designed for the composite medium grid photosensitive detector pixel in the readout circuit, which is used for discharging the overflow electrons of the collection area of the composite medium grid photosensitive detector after saturation, so as to improve the equivalent full well capacity. A special timing is designed, and the same pixel is read out twice in the analog domain and the digital domain to improve the equivalent full well capacity or reduce the readout noise, so as to improve the dynamic range. The composite medium grid photosensitive detector can obtain better detection effect in scenes such as astronomical observation and remote sensing monitoring which need to capture high-brightness and weak-light targets at the same time.

1. The name of the design product: small night light type rechargeable mosquito net ceiling fan. 2. The use of the design product: for mosquito net in the air blowing heat dissipation and weak light illumination. 3. The design points of the design product: in shape. 4. The picture or photo that can best show the design points: perspective drawing.

Operating an outward-facing display device for low light conditions may include receiving information indicative of ambient lighting conditions for the display device's environment; in accordance with a determination that the ambient lighting conditions do not satisfy a brightness criterion, determining a set of pixels in the outward-facing display device corresponding to an area of interest in the environment; and driving the set of pixels at a particular brightness to improve lighting in the area of interest.

System (100) for wearable smart glasses that provides improved visual support to visually impaired users, wherein the system (100) comprises: a frame (102) wherein the frame (102) is configured to accommodate electronic components; an image acquisition unit (104), wherein the image acquisition unit (104) is designed to be able to capture visual data in real time under low light conditions and daylight; a processing unit (106) wherein the processing unit (106) is functionally coupled to the image acquisition unit and is configured to execute deep learning models for image enhancement, object detection, extraction of prominent objects and text recognition; an augmented reality module (108) coupled to a processor, wherein the augmented reality module (108) is configured to overlay context-related digital information onto the user's field of vision; an IoT communication interface (110), wherein the IoT communication interface (110) enables real-time connectivity with external devices and cloud services; and an output interface (112), wherein the output interface (112) comprises audio feedback, haptic feedback or a tactile display designed to transmit detected information to the user.

A virtual eye test for assessing night blindness can be conducted in a virtual reality (VR) environment. The test utilizes an electronic device equipped with a head-mounted display (HMD) and a camera. The device generates a VR user interface corresponding to a three-dimensional virtual environment and renders it on the HMD. A plurality of visual scenarios, each corresponding to a different lighting condition, are simulated within the VR interface. Using the camera, the device tracks user interactions and responses to visual stimuli presented in these various lighting scenarios. Night blindness is measured based on the tracked user interactions and responses, providing a comprehensive assessment of visual performance under different low-light conditions in a controlled, immersive environment.

A weighted fishing bobber device is provided. The device is designed to enhance casting distance, stability, and bite detection, thereby improving fishing efficiency. The device comprises a slip bobber body with an aerodynamic tapered cylindrical shape to minimize air resistance. A hydrophobic coating may be applied to reduce drag in air and water. Stability and momentum of the device optimized through at least one weighted sinker. The device is also comprised of a three-way swivel that minimizes tangling and enhances functionality. The device may also be comprised of plastic beads that generate sound to attract fish, act as a buffer to protect structural components, and may feature luminescent properties for night fishing. Markings on the bobber body improve usability, including reflective indicators for low-light visibility and depth measurement indicators for bait positioning.

This application discloses a control system and a light-emitting bracket for indicating the location of a fire extinguisher, which is linked to a security alarm. The system includes: a micro central processing unit; a wireless receiving module for wireless communication with the security alarm; a wired connection module for wired communication with the security alarm; a light sensor for sensing ambient light; a low-light module for emitting a weak light to indicate the location of the fire extinguisher in low ambient light; and a high-light module for emitting a strong light to indicate the location of the fire extinguisher during a fire. When the smoke alarm detects a fire hazard, it emits an audible alarm to alert the user. Simultaneously, the smoke alarm sends an alarm signal to the control system. Upon receiving the alarm signal, the control system emits a strong light from the high-light module, allowing the user to clearly observe the location of the fire extinguisher. When the fire hazard is low or controllable, the user can quickly retrieve the fire extinguisher and extinguish the fire.

The application relates to the new energy technology field and provides a weak light early warning control method, an electronic device and a computer readable storage medium, which are applied to an electronic device connected with a photovoltaic module; the method comprises the following steps: acquiring an input current value of the photovoltaic module connected with the electronic device; when the input current value decreases, calculating a falling rate of the input current value according to the input current value sampled; if the falling rate is greater than or equal to a preset rate threshold value, updating a current reference value for performing weak light state judgment from a first current threshold value to a second current threshold value; the second current threshold value is greater than the first current threshold value. The above method can perform weak light early warning in time by controlling the current reference value, and improves the power utilization experience of a user.

This invention relates to the field of low-light image enhancement technology, and more particularly to a deep learning method for low-light image enhancement based on channel attention. First, for the input low-light image, a pixel illumination estimation module is fed into it for feature extraction. After gain estimation, the illumination adjustment coefficient required for each pixel is adjusted to obtain the gain coefficient for each pixel. The extracted features are multiplied by the gain coefficient to obtain a preliminary illumination map. The obtained preliminary illumination map is then fed into a channel attention block, which enhances the structural and pattern information in important channels and performs noise reduction. The results of the channel attention block are input into a fine-tuning module, which fuses and fine-tunes the features of different receptive fields with the original features to obtain the final features. The obtained features are then subjected to convolution, activation, and other operations. The low-light image and the normal illumination image are supervised using the L1 loss function and the similarity loss function, ultimately resulting in a high-precision normal illumination image.

The embodiment of the invention relates to the technical field of robot control, and provides a weak light environment target tracking method and system for an underwater robot. The method comprises the following steps: synchronously acquiring image data of an underwater environment; adaptive time sequence fusion, contrast enhancement and adaptive curve mapping processing are carried out on multiple frames of low-light visible light images which are continuously collected, and low-illumination enhanced images are obtained; extracting object surface texture features from the low-illumination enhanced image, extracting heat distribution features from the continuously acquired infrared images, and performing fusion processing on the object surface texture features and the heat distribution features through adaptive weight to generate a fusion feature image; performing identification and cross-frame continuous tracking on target sundries of a preset type in the fused feature image to obtain a target tracking result; and according to the target tracking result, controlling a motion control system and a cleaning device of the underwater robot to track and grab the target sundries in real time. Therefore, the target identification accuracy and tracking stability in the weak light environment are improved.

The invention provides an optical lens focusing processing method and device, equipment and a medium, and relates to the technical field of automatic focusing, and the method comprises the steps: obtaining the environment brightness information of a scene where imaging equipment is located at present, so as to determine a focusing strategy corresponding to the environment brightness information; collecting a target definition sequence according to the search resource configuration information in the focusing search process of the imaging equipment; under the condition that the definition trend feature corresponding to the target definition sequence does not meet the trend judgment condition, continuing to collect a new target definition sequence until the definition trend feature corresponding to the new target definition sequence meets the trend judgment condition; and controlling the imaging device to focus based on the new target definition sequence. The method can get rid of the dependence of a hill climbing algorithm on the definition value, and further improves the focusing speed and focusing accuracy in a low-light and low-contrast scene.

The method and device for denoising of extremely low light original image can improve the generalization ability of the joint denoising and demosaicing method for extremely weak light image, greatly reduce the number of parameters without losing performance, and have better robustness. The method comprises the following steps: (1) inputting an extremely low light original image into a system; (2) image reconstruction, reconstructing a single-channel original image into an RGBG four-channel image with a resolution of 1 / 4 of the original image; (3) black level normalization; (4) enhancing the signal at a specified magnification ratio; (5) inputting an SUnet++ neural network; (6) outputting an RGB*4 twelve-channel image with the same resolution as the image input into the neural network; (7) reconstructing the output image into an RGB three-channel image with the same resolution as the original system input image; and (8) outputting an RGB image with normal brightness.