118 results about "Haze" patented technology

The invention relates to the technical field of remote sensing monitoring, in particular to a remote sensing target detection method and system for a low-visibility image. The method comprises the following steps: acquiring multi-modal remote sensing image data; carrying out defogging enhancement processing on the low-visibility input image; normalizing the defogged RGB image and the defogged IR image, and then splicing and fusing the RGB image and the IR image; carrying out layer-by-layer coding on the multi-modal fusion image by adopting a mixed trunk structure fusing Transform, Mamba and CNN (Convolutional Neural Network); performing frequency domain decomposition on the trunk output features based on two-dimensional wavelet transform; generating an HR feature map by adaptively selecting a key region; and carrying out cross-scale aggregation on the HR feature map to obtain a detection target frame. Through the multi-modal image defogging enhancement and feature distillation mechanism, the definition and contrast of the remote sensing image in severe weather such as haze and rainy days are effectively enhanced, the shielding interference of environmental degradation on small target detection is weakened, and the stability and adaptability of the model in complex weather scenes are enhanced.

The invention discloses a traffic sign recognition and lane line detection method and system for extreme weather, and relates to the technical field of computer vision and artificial intelligence. According to the method, an efficient image restoration algorithm is designed, the influence of haze, rain and snow and other weather noise on the image quality is eliminated, and key detail information of traffic signs and lane lines is reserved; secondly, a YOLOv8 target detection network is improved, the problems of missing detection of small targets and false detection of complex backgrounds are solved, and the detection accuracy of the multi-scale traffic signs is improved; furthermore, a lightweight lane line detection algorithm is designed based on an improved line anchor mechanism, and the detection precision and real-time performance of the lane line in a complex scene are improved; and finally, an image restoration module, a traffic sign recognition module and a lane line detection module are integrated, multi-modal support of images, videos and real-time camera data is realized, an engineering solution adaptive to extreme weather is formed, and practical application of an intelligent traffic system in a complex environment is promoted.

The invention discloses a real haze image defogging method based on a haze degradation model. The method comprises the following steps: constructing a haze degradation model fusing multiple scattering effects and multiple image degradation factors; using the haze degradation model to construct a training data set including the clear image and the corresponding pseudo haze image; constructing a defogging network for a real haze scene; training the defogging network by adopting the training data set until a preset loss function is converged; and inputting a to-be-defogged image into the trained defogging network to obtain a defogging result. According to the haze degradation model constructed by the invention, the difference between a synthetic domain and a real domain is effectively relieved; a designed space-frequency hybrid module improves the adaptability of the model to complex degradation characteristics; the prior-guided feed-forward network fully excavates and fuses dark channel prior information, and the sensing and modeling capability of the model to the haze area is effectively enhanced.

The invention discloses a high-transparency PVB intermediate film and a production process and application thereof, and relates to the technical field of PVB intermediate films, the high-transparency PVB intermediate film comprises the following raw materials by weight: 100 parts of PVB resin, 20-30 parts of a composite plasticizer, 0.5-0.9 part of an LDH-based composite antioxidant, 0.5-1 part of an ultraviolet light absorber, 0.1-1.5 parts of nano lanthanum hexaboride, 0.1-1.5 parts of porous hollow nano copper sulphide, and 0.1-2 parts of nano TiO2 / SiO2 core-shell particles. The PVB intermediate film provided by the invention is relatively high in light transmittance, low in haze value, and excellent in oxidation resistance, weather resistance and mechanical strength.

The invention discloses a semi-solid-state complex no-tillage dry direct seeding method for crop seeds. The no-tillage dry direct seeding method comprises the following steps: S1, keeping a water layer of 1cm-5cm in a ditch, and paving straws obtained by harvesting preceding crops on a ditch surface; S2, applying a base fertilizer on the ditch surface paved with the straws and cleaning the ditch; S3, seeding the crop seeds on the ditch surface, and mixing the crop seeds with semi-solid-state biomass materials, a compound fertilizer and a biopesticide before seeding before seeding seeds, wherein the mixing mass ratio is that the crop seeds to the semi-solid-state biomass materials to the biopesticide is (1-10) to (70-800) to (1-3), and the moisture content of the semi-solid-state biomass materials is 40%-80%; and S4, after seeding the seeds, keeping the ditch surface at a dry state for 3-7 days. According to the semi-solid-state complex no-tillage dry direct seeding method, tillage and combustion of winter crop straws are not carried out, a whole land of field immersing water is not used for irrigation and the water immerses after the seeding, and the biological benefits are obvious; the working energy consumption is reduced, stubbles are not killed by pesticides, haze is reduced and the irrigation water is reduced.

The invention provides a dust raising haze treatment system and method for environmental protection. The system comprises an environment parameter real-time monitoring module, a flying dust diffusion dynamic modeling module, a multi-mode treatment strategy generation module, a high-pressure atomization dust suppression execution module, an electrostatic adsorption purification module, an intelligent dynamic feedback control module, an energy optimization distribution module and a remote collaborative management platform module. According to the scheme, the turbulence correction model, deep reinforcement learning control and gradient electric field design are systematically fused, and the contradictory problem of low prediction precision and high energy consumption in the traditional technology is solved.

The invention relates to the technical field of atmospheric transmission characteristics, and provides a method for acquiring atmospheric transmittance in haze weather, which comprises the following steps of: acquiring a mixed composition ratio of a plurality of preset haze particle groups forming a preset aerosol type, and acquiring diameters and complex refractive indexes of a plurality of wet haze particles contained in the plurality of preset haze particle groups; and determining the extinction efficiency factor of each wet haze particle according to the diameter and the complex refractive index. And determining the total extinction coefficient of the preset aerosol type according to the particle distribution function of the preset aerosol type, the mixing composition ratio and the extinction efficiency factors corresponding to the plurality of wet haze particles. According to the total extinction coefficient, the atmospheric transmittance is calculated through an atmospheric transmittance calculation function. The calculation of the atmospheric transmissivity is carried out based on the diameter and the complex refractive index of the wet haze particles, and the influence of the preset aerosol type composed of the plurality of preset haze particle groups on the calculation of the atmospheric transmissivity is considered, so that the calculation accuracy of the atmospheric transmissivity is improved.

The invention discloses a mine dust fog image defogging method and system based on three-dimensional inverse interactive intersection, and the method comprises the steps: adding a three-dimensional attention extraction module in a u-net network architecture, and adding a space regularization inverse cross attention and inverse interactive connection module in a downstream task; the three-dimensional attention extraction module enhances the sensing ability of the model to non-uniform haze; the spatial regularization inverse cross attention module dynamically and adaptively highlights residual fog, artifacts and contrast loss areas, enhances the response of the depth estimation network to the areas, and improves the overall performance and robustness of double tasks; the inverse interactive connection module improves the cooperation efficiency of the defogging and depth estimation network, and also solves the problem that error information cannot be fed back to a feature layer. The system is an encoder-decoder architecture, and a three-dimensional attention extraction module, an inverse cross regularization attention module and an inverse interaction connection module are arranged in the encoder-decoder architecture. According to the invention, the accuracy of a monitoring camera system and an intelligent detection system in a mine is improved.

The invention provides a power equipment defect detection system and method based on a multi-mode Transform, and the system comprises a double-flow feature extraction module which is used for carrying out the feature extraction of a collected visible light image and an infrared image of power equipment through employing a double-flow feature extraction network based on YOLOv5, and obtaining a visible light feature pattern and an infrared feature pattern; the multi-modal information interaction module is used for carrying out multi-modal information interaction on the visible light characteristic pattern and the infrared characteristic pattern to obtain a multi-modal fusion characteristic pattern; and the defect classification module is used for carrying out defect detection and classification on the power equipment by adopting a two-stage residual full-connection defect classifier based on the multi-modal fusion feature map to obtain a defect classification result. The method can overcome the influence of environmental factors such as heavy fog, rain, haze and the like, is not limited by illumination conditions, and has relatively high anti-interference capability. In addition, the method can also solve the problems of low resolution and fuzzy texture details of the infrared image.

The invention discloses an intelligent networking driving recording system based on multi-mode perception. The intelligent networking driving recording system comprises a perception layer, a processing layer, an application layer and a block chain evidence storage module. The invention further discloses a data tamper-proofing method of the intelligent networked driving recording system based on multi-mode perception, which comprises the following steps: S1, synchronously acquiring thermal radiation data, radar point cloud data and video image data in a driving environment through the infrared thermal imaging sensor, the 4D millimeter wave radar and the high-definition optical camera; s2, infrared thermal imaging data are optimized, radar point cloud clustering is optimized through a DBSCAN algorithm, and target tracking is achieved based on UKF. The system can cover the whole temperature range of-30 DEG C to 85 DEG C and severe weather such as rain, snow and haze, effectively reduces the false alarm rate of infrared thermal imaging and the missed detection rate of a millimeter wave radar static target, avoids the loss of accident key evidence caused by environmental factors, greatly improves the evidence obtaining reliability at night and in severe weather, and reduces traffic accident disputes.

This invention discloses a method and system for intelligent weather image acquisition and recognition, specifically relating to the field of weather image recognition technology. It involves continuous polarization imaging of the sky region while simultaneously acquiring spatial attitude and temporal information; preprocessing the polarization image data and extracting Stokes parameters to obtain standardized sky polarization light field data; then using a theoretical Rayleigh scattering model to analyze the polarization degree distribution pattern at different wavelengths to obtain theoretical sky polarization pattern characteristics; based on the polarization light field data, identifying the spatial distribution of actual clouds, aerosols, and haze in the sky to form actual atmospheric composition distribution characteristics; extracting anomalous polarization regions through difference analysis between theoretical and actual characteristics, and accurately classifying halo polarization anomalies and scattering polarization anomalies based on geometric and spectral characteristics; generating ultralocal weather change early warning signals based on the classification results; thus improving the accuracy and real-time performance of early warnings for ultralocal anomalous weather phenomena.

The invention relates to a track defect detection method based on an infrared image under a low-visibility condition. The method comprises the steps that active pulse type thermal excitation is applied to the surface of a track, an infrared image sequence and a visible light image are synchronously collected and preprocessed, feature level fusion is carried out on the preprocessed infrared image and visible light image, track defect recognition is carried out in combination with an improved cross-scale polymorphic self-adaptive detection network, and the track defect recognition accuracy is improved. Outputting a defect bounding box and a corresponding defect category label; and then sub-pixel-level edge contour positioning processing is carried out on the defect area, geometric parameters of the defect area are calculated based on a positioning result, and a track defect detection report is generated by combining the geometric parameters, a defect category label and a track structure health state evaluation result. By adopting the method, the bottleneck of failure of visible light detection in a low-visibility environment can be effectively overcome, and high-precision and high-efficiency detection and evaluation of track defects in severe environments such as night, haze, rain and snow and the like can be realized.

The application discloses a deep learning-based artillery barrel target image defogging method and system, which comprises the following steps: inputting an original artillery barrel target image into a trained defogging enhancement network model for processing, wherein the defogging enhancement network model comprises an improved U-Net module, an AOD-Net module and an atmospheric scattering model layer connected in sequence; the improved U-Net module is used to extract multi-scale features of the original artillery barrel target image, and output the multi-scale feature maps to the AOD-Net module; the AOD-Net module is used to extract features under different receptive fields of the multi-scale feature maps through different convolution layers, realize multi-scale feature fusion, and output input values of the atmospheric scattering model layer; and the atmospheric scattering model layer is used to obtain the defogged artillery barrel target image based on the input values. The model is constructed by connecting the U-Net and the AOD-Net in series, a new model optimizer is proposed, the algorithm of gradient descent is optimized, and a self-adaptive weight loss function is introduced, so that efficient defogging processing under complex fog and haze conditions is realized.

The method for removing fog based on inter-layer multi-scale sequence interaction and Fourier domain frequency-space enhancement comprises the following steps: step S1: collect a public fog data set containing different concentrations of fog and haze, and pre-process the images therein to improve data quality and model training effect; step S2: construct a network framework for removing fog based on inter-layer multi-scale sequence interaction and Fourier domain frequency-space enhancement, which comprises a U-shaped fog removal network, an inter-layer multi-scale sequence interaction module IMSIM and a Fourier domain frequency-space enhancement module FDFSEM; step S3: train the network using the labeled image data set, define a suitable loss function and use an optimizer to train; step S4: evaluate the fog removal effect using two indicators of peak signal-to-noise ratio PSNR and structural similarity index SSIM, optimize the trained model, and deploy it to the actual application for online inference and real-time fog removal; through the above steps, the input feature map can be removed.

The invention relates to the field of high polymer materials, in particular to a high-definition perspective TPEE film, a preparation method and application. The invention discloses a high-definition perspective TPEE film which is prepared from the following components in percentage by weight: basic resin TPEE, a high-definition additive, a weather-resistant stabilizer, an antioxidant, an antistatic agent, an anti-scraping agent and a compatilizer, and further provides a preparation method of the TPEE film, the preparation method comprises the following steps: pre-mixing the components, performing melt blending through a twin-screw extruder, and finally forming the film through a tape casting method or a film blowing method, so as to obtain the high-definition perspective TPEE film. Meanwhile, the invention discloses application of the TPEE film in a high-definition display screen, a high-end packaging material or special optical equipment. The prepared TPEE film has high-definition perspective performance, the film thickness is 50-200 microns, the light transmittance is larger than or equal to 91%, the haze is smaller than or equal to 1.2%, the yellowing index delta b is smaller than or equal to 0.6, and the TPEE film has the effects of weather resistance, oxidation resistance, static electricity resistance, scraping resistance and the like.

The invention discloses an experimental device for simulating surface dirt accumulation and detection of an intelligent coal caving camera, and relates to the technical field of coal mine engineering, the experimental device comprises an air duct model group, a dust generator, an air draft mechanism, a power supply, a controller, a sensor assembly, a haze meter and a plurality of camera type dust collectors; the air duct model group with the transparent side wall is provided with a simulation air duct for simulating a coal caving space at the rear part of an underground fully mechanized caving face; the air draft opening of the air draft mechanism is connected with the outlet of the simulation air duct and has adjustable power; the sensor assembly comprises a wind speed sensor and at least one concentration sensor; the camera type dust collectors are respectively arranged at different positions of a dust flowing path; the haze meter is used for detecting the haze of each simulation lens for at least three times, the controller is in communication connection with the dust generator, the air draft mechanism, each sensor and the haze meter, and the power supply is used for powering on each component. The system can simulate the coal dust attachment characteristics on the surface of the camera, provides support for the research of reducing the dust attachment of the camera, and improves the precision of a visual system of an underground intelligent coal caving machine.

The invention relates to the field of environmental monitoring, and discloses a PM2.5 concentration weighting-based dust-haze discrimination standard construction method, equipment and a medium. Compared with the existing three common dust-haze discrimination standards for setting a single threshold value based on visibility and relative humidity, the PM2.5 concentration weighting-based dust-haze discrimination standard construction method has the advantages that the PM2.5 concentration weighting-based dust-haze discrimination standard construction method is established by establishing the relationship between the PM2.5 mass concentration and the visibility and the relationship between the PM2.5 mass concentration and the relative humidity; and analyzing the occurrence frequency of fine particle aerosol pollution, providing a new standard of assigning different threshold values to different relative humidity intervals, and accurately identifying the haze day. According to the method, the characteristics of dust-haze fine particle aerosol pollution can be better represented, and fine particle aerosol pollution and non-fine particle aerosol pollution can be more accurately distinguished.

The present invention relates to a method for removing haze from land, sea and air battlefield images based on parametric modeling and gradient guidance. Natural battlefield images and remote sensing battlefield images are collected, and fog synthesis strategies are used to generate natural battlefield haze-removing datasets and remote sensing battlefield haze-removing datasets, and an underwater image enhancement dataset is obtained. A battlefield image haze-removing network is constructed, in which the fog concentration estimation network combines the dark channel prior and the Swin Transformer model to generate fog concentration parameters, the medium transmission estimation network estimates the medium transmission parameters based on the encoder-decoder model, and the ambient light estimation network collaborates with the encoder and local minimum filtering to estimate the ambient light parameters. The estimated parameters are substituted into the imaging model, and the imaging model is inversely solved to generate the battlefield haze-removing result. A gradient guidance module is used to further enhance the image details, and clear and detailed battlefield images are obtained. The present invention integrates the complementary advantages of physical model methods and deep learning methods, effectively improves the visual effect of battlefield fog images, and can achieve multi-scene haze removal in a single framework, which is convenient for popularization and use.

The embodiment of the invention provides a haze weather image processing method and device based on a deep convolutional network, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-processed original haze image, carrying out the channel processing of the original haze image, generating an R-channel image, a G-channel image and a B-channel image, calling a feature extraction algorithm, carrying out the recognition of the R-channel image, the G-channel image and the B-channel image, and carrying out the recognition of the G-channel image and the B-channel image. Generating a first target image according to the G channel image and the B channel image, generating a second target image according to a multi-scale mapping algorithm, a local extremum algorithm and the first target image, and calling a bilateral correction linear unit to generate a target defogging image corresponding to the second target image, the defogging effect is enhanced by calling a feature extraction algorithm, a multi-scale mapping algorithm, a local extremum algorithm and a bilateral correction linear unit.

The application discloses an automatic haze detection device and relates to the technical field of haze detection devices. The device comprises two sets of mechanical hands, a tightening system, a control system and a bottom plate. The bottom plate is provided with a support frame, and the support frame is provided with two sets of conveying lines. The two sets of conveying lines convey a film therebetween. The film is connected with the tightening system at both ends. A support plate is installed on one side of the two sets of conveying lines. A driving motor is installed on the support plate. The output shaft of the driving motor penetrates through the support plate and is provided with a driving gear. A driven gear is engaged on one side of the driving gear. A spraying cylinder is installed on one side of the support plate. Two sets of haze meters are installed on one side of the spraying cylinder. The two sets of mechanical hands are respectively located on one side of the two sets of haze meters. The two sets of mechanical hands are both provided with auxiliary diaphragms. Air is sprayed on the surface of the film through air blowers, dust on the film is cleaned, and composite cleaning is formed with the brush, so that the cleaning effect of the film at the position to be detected is improved.

The application relates to the technical field of image cleaning, and particularly discloses a power transmission and transformation equipment image enhancement and defect identification method for severe weather, the image enhancement method comprising the following steps: collecting power transmission and transformation equipment image quality data and environment data at each image acquisition time through a data acquisition module; combining the environment data at each image acquisition time to clean and correct the quality data of the power transmission and transformation equipment image, so that the reliability and rationality of the power transmission and transformation equipment image quality data are improved, the accuracy of calculating the haze influence index of the power transmission and transformation equipment image at each image acquisition time is improved, and the rationality of the haze influence degree evaluation result at each shooting time is improved; on the basis, the contrast of the image is compensated by combining the haze influence index with high accuracy, so that the distinguishing degree of the power transmission and transformation equipment and the background is enhanced, the accuracy of subsequent image defect identification is improved, and the power transmission and transformation equipment image is enhanced.

This invention belongs to the field of photovoltaic-storage-charging microgrid technology, and provides an anomaly monitoring method and system for photovoltaic-storage-charging microgrid systems. It addresses the problem of inability to accurately detect or detect the impact of haze on power generation. This invention performs a first data correction on photovoltaic panel power generation based on haze intensity; a second data correction based on haze values ​​influenced by wind speed; a third data correction based on haze values ​​influenced by humidity; and a fourth data correction based on haze values ​​influenced by temperature. Anomalies are then detected using the four corrected photovoltaic panel power generation figures. This invention achieves anomaly detection when haze causes a decrease in power generation, and considers multiple factors such as environmental wind speed, temperature, and humidity that cause data changes, determining the power generation correction values ​​under the influence of each factor, greatly improving the accuracy of anomaly monitoring and detection.

The invention discloses a vehicle driving control method and device, a mine car, a storage medium and a product, and the method comprises the steps: obtaining a dust recognition result and a haze recognition result of an environment in a vehicle driving process through image collection equipment and an Internet of Things platform; through a rainfall sensor and an Internet of Things platform, obtaining a rainfall identification result of an environment in which the vehicle is located in a driving process; according to the sand and dust recognition result, the haze recognition result and the rainfall recognition result, weather information of the environment where the vehicle is located in the running process is determined, and a control strategy matched with the weather information is adopted to control the speed limiting value and the braking force of the vehicle. According to the technical scheme, the safety coefficient of the mine car under different weather conditions can be improved, and the operation efficiency of the mine car is guaranteed.

The invention discloses a dust and haze multivariable collaborative regional dynamic comprehensive evaluation method based on site observation and grid live analysis products, which combines the difference of physical and chemical properties of meteorological elements and particulate matters in dust, fog and haze weather, and introduces a Mie scattering theory to calculate an aerosol extinction coefficient to diagnose fog and haze. A scientific key characteristic index threshold value is adopted, and a multivariable cooperation and regional dynamic comprehensive evaluation method is used for diagnosis; finally, the three diagnosis results of direct diagnosis and site and grid point indirect diagnosis are comprehensively judged, the uncertainty of single data source diagnosis is reduced to the maximum extent, the evolution process, the influence range and the intensity change characteristics of sand, dust, fog and haze weather can be well represented, the temporal-spatial resolution is 5 km / 1 h, and the time efficiency lags for 55 min.

The application discloses a kind of high hardness, high weather-resistant TPU material and preparation method thereof, the TPU material includes by weight fraction: 30~60 parts cyclohexane polyol, 30~60 parts cyclohexane diisocyanate, 0.3~2 parts antioxidant, 0.05~1 parts lubricant, 0.05~0.5 parts wax powder, 0.01~0.5 parts catalyst.The application adopts double symmetry cyclohexane aliphatic ring structure, molecular chain arrangement is neat, phase region size is less than visible light wavelength, with high hardness, high transmittance and low haze;Pure aliphatic ring structure eliminates yellowing from the source, and has excellent weather resistance and aging resistance;Double cyclohexane skeleton forms steric barrier, has outstanding hydrolysis resistance, acid and alkali resistance, fuel resistance, while high temperature mechanical retention rate, creep resistance and dimensional stability are excellent, and can be widely used in diesel filter oil cup, high temperature sealing element, outdoor transparent structural member and other high-end scenes.

The invention discloses a bridge side falling behavior recognition method and system in a haze environment, and the method comprises the steps: carrying out the training of a pre-constructed RA-M fusion coding and decoding recognition network model based on a bridge side falling behavior data set and a model loss function in the haze environment; the trained RA-M fusion coding and decoding recognition network model is adopted to recognize the bridge side falling behavior in the haze environment; wherein the RA-M fusion coding and decoding identification network model comprises an RA-M encoder module and an RA-M decoder module, the RA-M encoder module is used for carrying out multi-scale feature extraction and fusion on images, and the RA-M decoder module is used for pedestrian masking and position recovery based on a reverse attention mechanism. According to the invention, the position and behavior state of a falling pedestrian can be accurately identified in a haze environment, bridge side falling behavior monitoring under a severe weather condition is realized, early warning information is provided for rescue workers in time, and the rescue response speed and success rate are improved.

The invention provides an unmanned aerial vehicle aerial image defogging method based on improved AOD-Net, and aims to improve the defogging effect of an unmanned aerial vehicle aerial image in a haze environment. According to the method, a Pseudo-Huber loss function and total variation regularization are combined to construct a PH-TV composite loss function, and a traditional L2 loss function is improved; the AOD-Net is inserted into a space channel interaction cooperation SCSIA module, multi-semantic-level cooperation of space and channels is achieved, and extraction of the model on the feature dependency relation and space structure of unmanned aerial vehicle aerial images is optimized; a multi-scale expansion fusion module is added, different expansion rates are set, normalization processing is carried out, and semantic information of aerial images of the unmanned aerial vehicle under different scales is captured. The method can effectively recover the color and structure details of the aerial image of the unmanned aerial vehicle under the foggy weather condition, and has a wide application prospect in the field of aerial photography of the unmanned aerial vehicle.

The present application relates to a kind of micro-light image enhancement method suitable for haze climate, belong to digital image processing field.First, the method can effectively learn the denoising information in image pair in the process of learning through GCA (Group Channel Attention) group channel attention structure, can solve the interference caused by external noise;Second, the transformer block of the method makes the network have better window adaptive ability through self-attention mechanism, can solve local overexposure phenomenon with better effect;Third, the method can be fine-tuned by a small amount of field samples, greatly improve the adaptability to the required environment, and can learn new tasks on the task of low-light enhancement, solve the influence of non-illumination factors on image quality.