21 results about "Automatic threshold" patented technology

The invention relates to the technical field of industrial nondestructive testing, in particular to a wall thickness detection device and method based on a dual-ray system, and the method comprises the steps: synchronously shooting vertical and horizontal projection images of a pipe through a dual-ray source; extracting first line gray data of the bidirectional projection image, and capturing an edge position through an automatic threshold method; grabbing inflection points of the inner wall and the outer wall of the pipe according to the edge position, and totally obtaining eight inflection points; calculating inner and outer diameters of the pipe by using a mathematical model based on the inflection point position Calculating the wall thickness, the ovality, the eccentricity value and the concentricity according to the inner diameter and the outer diameter; according to the invention, full detection in the pipe production process is realized, the detection efficiency is high, the influence of slight shaking of the pipe can be resisted, the detection result is accurate, and the device is suitable for detecting the wall thickness and related quality indexes of linear materials such as PVC pipes and cables.

The invention relates to the technical field of intelligent design of optical lenses, and discloses a multi-series superposed complex periodic structure arrangement method and system.The method comprises the steps of normalized coordinate transformation, multiple sine wave superposition, an iterative function system, a Julia set algorithm, an LSystem grammar system, parameter adjustment, moire fringe fusion and the like; and a complex periodic structure with high complexity, adaptability resistance and natural morphological characteristics is generated. Advanced technologies such as logarithm uniform distribution wave number generation, a chaos game algorithm, linear mapping complex number conversion, a turn graph interpreter, multi-scale entropy complexity quantification and an Otsu automatic threshold method are adopted, and accurate parameterization control and manufacturing adaptation are achieved. The system comprises a coordinate transformation module, a wave interference calculation module, a fractal generation module, a complex power system module, an LSystem processing module, a parameter control module and a contour surface extraction module. According to the invention, a systematic solution can be provided for complex structure design of optical devices such as myopia prevention and control lenses.

The invention relates to a three-dimensional modeling method for a reef limestone microstructure, which comprises the following steps of: scanning to obtain an original grayscale image of a rock core sample, and preprocessing the original grayscale image; based on the preprocessed grayscale image, extracting a sample shell mask; establishing a Gaussian mixture model of sample gray level distribution, and determining a segmentation threshold value; classifying and segmenting the volume data through the segmentation threshold to obtain a pore candidate region, a matrix candidate region and a pending region; performing morphological closed operation on the candidate region to obtain a pixel set belonging to the pore and a pixel set belonging to the matrix; and classifying and segmenting the pending region by adopting an energy minimization algorithm based on graph segmentation. According to the method, the GMM dual-threshold segmentation and shell mask construction technology is adopted to accurately extract the core area, and air at the core boundary is prevented from being wrongly segmented into pores, so that the real boundary of the core is completely reserved; according to the method, the pores can be finely judged, the automatic threshold segmentation result is stable and reliable, and manual intervention is greatly reduced.

The application discloses a power operation safety evaluation system based on big data, which comprises a safety evaluation data acquisition module, a multi-dimensional safety feature selection module, a dynamic safety evaluation model construction module and an intelligent safety evaluation module. The application relates to the technical field of power safety data processing, and specifically relates to a power operation safety evaluation system based on big data. The application innovatively introduces a self-encoder model, accurately quantifies the nonlinear correlation between features by combining cosine similarity, determines an optimal sparse correlation graph through an automatic threshold adjustment algorithm, generates feature clusters by algorithm clustering, automatically identifies the optimal clustering structure, filters out double-optimal core features in terms of applicability and centrality, significantly reduces redundant features, enhances the output result accuracy of the evaluation model, and constructs a dynamic safety evaluation model based on the multi-view construction of feature clusters and the fusion of a double-cycle safety evaluation unit, thereby significantly improving the accuracy and stability of the power operation safety evaluation model.

The application provides a computer vision-based structural vibration displacement monitoring method and system and a storage medium, and the method comprises the following steps: step 1, structural vibration video preprocessing; basic information of video data is automatically acquired by using a video basic information automatic acquisition algorithm, and an image gray scale and local histogram equalization processing method is used to simplify the image calculation complexity and enhance the image features; step 2, structural displacement monitoring based on improved FAST corner detection; an automatic threshold calculation method based on the maximum inter-class variance is introduced to form an improved FAST corner detection algorithm, the image features obtained by the improved FAST corner detection algorithm are combined with the pyramid Lucas-Kanade optical flow method, and a whole-process automatic image feature detection and motion tracking scheme is established. The method has the advantages that the overall monitoring process is optimized, and the method is non-contact, simple to operate and high in automation.

The application discloses a four-wave transverse shearing interferogram effective area segmentation system and method based on FFT and global threshold, and belongs to the technical field of optical interference measurement and wavefront sensing. The method comprises an image acquisition module, a preprocessing module, an FFT low-frequency extraction module, an automatic threshold segmentation module and a morphological optimization module. The application carries out smooth processing on the background brightness distribution of the interferogram through FFT low-pass filtering, and realizes automatic segmentation of the effective area in combination with Otsu global threshold, effectively avoids technical defects such as center area inversion blackening, edge fracture and excessive noise that are prone to occur in traditional area segmentation methods, can completely extract the four-wave overlapping area, has the advantages of stable segmentation, strong anti-interference capability, no need for manual parameter adjustment, good real-time performance and the like, and can be widely applied to various four-wave transverse shearing interferometer wavefront detection systems.

The application discloses a pretreatment method suitable for liquid-phase plasma discharge spark image recognition, and specifically comprises the following steps: step 1, an industrial camera is used to take pictures of the surface of a workpiece to obtain a to-be-processed spark image; step 2, the spark image obtained in step 1 is subjected to grayscale processing to obtain a grayscale histogram; step 3, a background correction method based on a rolling ball algorithm is used to correct the background of the to-be-processed spark image subjected to grayscale processing in step 2; and step 4, an optimal segmentation threshold is obtained by using an automatic threshold segmentation method based on a grayscale mean value and a grayscale standard deviation, the spark and the background in the spark image processed in step 3 are segmented, and a target spark image is obtained. The application solves the problem that the existing threshold segmentation method cannot accurately segment small target discharge sparks in a complex background with high grayscale levels, uneven image brightness and a multi-peak grayscale histogram.

A system and method for predictive maintenance of operational mechanisms using dual-stage artificial intelligence analysis of optical sensor data. The system comprises optical sensors capturing images, a hardware interface, and a processor implementing two specialized AI models: a first model generating structured technical captions describing physical conditions, and a second model identifying mechanical anomalies from these captions. The system features dynamic control parameter modification, automatically implementing adjusted operational parameters based on detected anomalies until maintenance completion. Real-time severity assessment enables quantitative evaluation with automatic threshold modifications to prevent mechanical failure. The system generates comprehensive maintenance alerts including technical specifications, sensor data, time-to-failure predictions, and maintenance procedures. Implementation options include local, cloud-based, or hybrid processing configurations. Applications span critical transportation systems, defense platforms, and industrial equipment, providing continuous monitoring, predictive maintenance, and automated control adaptation to prevent equipment failure.

The application relates to an FPGA-based implantable electrode Spike signal detection system and method, which uses FPGA to collect brain electrical data signals transmitted by an implantable electrode chip, carries out filtering processing and automatic threshold feature extraction on the collected brain electrical data, completes the Spike extraction task, and sends the processed brain electrical data and the original data to an upper computer through a PCI-E mode. Compared with the prior art, the application adopts FPGA parallel processing data, integrates the Spike signal detection method on the FPGA, greatly reduces the real-time detection speed of the Spike, adopts a filter to pre-process the brain electrical data, can effectively retain Spike effective signals, adopts an automatic threshold feature extraction method, can adaptively extract Spike signals according to brain electrical signal fluctuations, not only reduces the power consumption of the chip, but also guarantees the accuracy of Spike signal extraction.

The application provides a kind of based on scanning electron microscope image deep-sea sediment particle three-dimensional structure reconstruction method: obtain the scanning electron microscope image of deep-sea sediment particle and carry out gray scale, filtering and contrast enhancement;Based on automatic threshold segmentation and connected domain analysis lock target particle area, and synchronous cutting gray scale and binary image;To target particle binary mask is maximum connected domain reservation, hole filling and size standardization processing;Gray value is mapped to relative height and shield background area, generate only cover target particle height field;Based on the height field generates multi-view three-dimensional graph, color height distribution graph, center profile, error band, local refinement reconstruction graph and height classification graph.Through the technical scheme of the application, the efficiency and stability of particle morphology contrast analysis can be improved without changing the existing scanning electron microscope hardware, realizing the automatic extraction, background shielding, three-dimensional morphology reconstruction and quantitative expression of deep-sea sediment particles in a single SEM image.

The invention discloses a battery pack EOL test data processing and judging method, system and device and a medium. The method comprises the following steps: acquiring basic information and configuring a test mode; automatically identifying and positioning an original data workbook; carrying out secondary calibration on the number of multi-packet combination packets and dividing data column segments; accurately positioning the working steps and extracting key data; dCIR key indexes are automatically calculated and summarized; carrying out dynamic test automatic identification and typing calculation; automatic threshold value judgment and abnormal popup alarm are carried out; and automatic filing, anti-overlay storage and MES synchronous uploading are realized. The method can adapt to the existing data generation rule of the test equipment, reduces the manual dependence, and improves the consistency and traceability.

The application discloses a detection device and method for positive and reverse surfaces of an interference type ultra-small curvature plano-convex lens or plano-concave lens. The detection method comprises the following steps: obtaining an interference image of a surface of a sample to be detected; and determining whether the surface is a curved surface or a flat surface according to the shape of the interference image. The detection method is simple, easy to operate, high in efficiency and 100% in accuracy. The detection device for positive and reverse surfaces of the interference type ultra-small curvature plano-convex lens or plano-concave lens is simple in structure and convenient to use. After the optical system with the specific structure is used, the interference image of the surface of the lens can be obtained by using a plane array camera, and whether the surface is a curved surface or a flat surface can be determined according to the shape of the obtained interference image. Further, after the interference image is sequentially subjected to Fourier transform and automatic threshold value calculation related to a mode, the difference between the calculation results of the curved surface and the flat surface can be used to realize automatic reading of the curved surface and the flat surface.

Embodiments of digital polymerase chain reaction (dPCR) systems and methods with improved automatic threshold performance and accuracy are disclosed. One embodiment of the present disclosure includes a dPCR processing module that includes an automatic threshold proxy that dynamically analyzes and selects between Gaussian mixture model (GMM) and K-means clustering for automatic threshold results of dPCR assays using a dPCR instrument to improve dPCR measurement techniques.

The invention belongs to the field of cornu cervi pantotrichum decoction piece identification, and particularly relates to a cornu cervi pantotrichum decoction piece identification method based on image identification. The system comprises a pilose antler decoction piece image feature extraction module, a pilose antler decoction piece image pore identification module and a pore statistics output module. Through an OpenCV technology, preprocessing and pore feature extraction are carried out on a cornu cervi pantotrichum decoction piece image, a quantitative index system of pore size, number and distribution density is constructed, a grading standard based on a pore feature threshold is established, and through automatic threshold determination, cornu cervi pantotrichum decoction piece image data can be processed in batches and a standardized grading result can be output. Compared with traditional artificial experience judgment, the method is suitable for quality evaluation and grade division in industrial production. Particularly, the problem that the classification accuracy is not high due to the fact that the characteristics of the transition area samples of the cornu cervi pantotrichum decoction pieces are not obvious is solved through the method of visualization and quantification of the transition area samples of the cornu cervi pantotrichum decoction pieces. Furthermore, the standardized, large-scale and featured development of the deer industry is promoted, and the standardization of the pilose antler traditional Chinese medicine is promoted.

The invention discloses a character stroke disconnection defect detection method and device, electronic equipment and a medium, and relates to the technical field of image recognition. The method comprises the following steps: acquiring a real-time character image and a template character image; performing global automatic threshold segmentation on the real-time character image to obtain a first binary image, and performing defect detection operation on the first binary image to obtain a few-print defect detection result of the first binary image; performing secondary binarization processing based on a character region on the real-time character image to obtain a second binary image, and performing defect detection operation on the second binary image to obtain a damage defect detection result of the second binary image; and based on the underprint defect detection result and the damage defect detection result, executing overlapping judgment, and outputting a stroke disconnection defect detection result of the real-time character image. With the adoption of the method, the detection precision can be ensured, the tiny disconnection defects of character strokes can be accurately identified, and the reliability and precision of character defect detection are improved.

This invention belongs to the field of intelligent cooking control technology and discloses an intelligent detection method for the cooked doneness of pan-fried basa fish based on multimodal data fusion. The steps are as follows: A sample for creating a cooked doneness classification model of pan-fried basa fish is prepared. Hyperspectral data, infrared thermal imaging data, and electronic nose data of the pan-frying process are collected simultaneously, and automatic threshold segmentation and filtering denoising preprocessing are performed on the multimodal data respectively. Multi-branch convolutional feature extraction modules are designed for hyperspectral, infrared image, and electronic nose signals respectively. A Bayesian-optimized hyperparameter automatic search strategy is designed to improve model performance. Relative position encoding and sparse attention mechanism are introduced into the Transformer network structure to construct a basa fish cooked doneness MBCE-BO-RP-Transformer classification model. The classification accuracy and lightweight performance of the model are tested and evaluated.

The application discloses a kind of mixed intelligent driving three-dimensional point cloud change detection method, comprising the following steps: double time phase point cloud input and pre-processing;Cross-period corresponding relationship between double time phase point cloud is established using neighborhood search;Multi-scale normal distance geometry prior calculation;Texture difference prior calculation;Automatic threshold and normalization processing are carried out to the above calculation result;Density consistency suppression and prior score construction;Change perception difference attention coupling is carried out to the double branch coding-decoding network constructed and prior knowledge injection;Change perception enhancement features are obtained;Decoding output change detection result.The application significantly enhances the attention and expression to the real change area without changing the structure of the main network, effectively reduces the false detection caused by density difference, occlusion and noise, and realizes high-precision detection of the real change area in double time phase urban three-dimensional point cloud, and improves the adaptability and robustness of the method under complex occlusion, inconsistent density and multi-source data conditions.

The present application relates to a kind of image quality improvement methods, specifically to a kind of method for reducing EBCMOS gain image halo.The present application solves the technical problem that the halo generated by the imaging of the existing EBCMOS detection device is difficult to improve.The method of the present application comprises the following steps:1) sampling the image to obtain electron group;2) feature recognition;2.1) the electron group of each sampling point is identified and separated into multiple HxF pixel size small clusters by the identification system;2.2) using interclass variance method, determine the halo cluster B of segmented halo and target, and the gray mean of halo cluster B is b;3) using global automatic threshold comparison method, the gray mean of small cluster is compared with the gray mean b of halo cluster B for first-level comparison, filter out halo cluster, and leave target cluster for second-level comparison;4) using particle swarm algorithm, the target cluster filtered out halo cluster is compared for second-level comparison, and eliminate backscattering cluster;5) output optimized small cluster, and reconstruct low-halo target image.

The invention belongs to the technical field of intelligent cooking control, and discloses a multi-modal data fused fried Basa fish processing cooking degree intelligent detection method, which comprises the following steps: preparing a sample of a fried Basa fish processing cooking degree classification model, synchronously collecting hyperspectral data, infrared thermal imaging data and electronic nose data in the frying process, and calculating the data of the frying process according to the hyperspectral data, the infrared thermal imaging data and the electronic nose data; carrying out automatic threshold segmentation and filtering and denoising preprocessing on the multi-modal data; respectively designing multi-branch convolution feature extraction modules for hyperspectrum, infrared images and electronic nose signals; designing a hyper-parameter automatic search strategy based on Bayesian optimization to improve the performance of the model; a relative position coding and sparse attention mechanism is introduced into a Transform network structure, and a Basa fish frying cooking degree MBCE-BO-RP-Transform classification model is constructed; and model classification accuracy and lightweight performance are tested and evaluated.

The present application relates to a kind of reef limestone mesostructure three-dimensional modeling method, comprising the following steps: scanning and obtaining the original gray image of core sample, and pre-processing;Based on the gray image after pre-processing, extract sample shell mask;Establish the Gaussian mixture model of sample gray distribution, determine the segmentation threshold;Through the classification segmentation of body data by segmentation threshold, obtain pore candidate area, matrix candidate area and undecided area;Morphological closing operation is carried out to candidate area, and the pixel set belonging to pore and the pixel set belonging to matrix are obtained;For undecided area, energy minimization algorithm based on graph cut is used for classification segmentation.The present application adopts GMM double-threshold segmentation and shell mask construction technology to accurately extract core area, avoid air at core boundary being misclassified as pore, so that the real boundary of core is completely retained;The present application can finely determine pore, and the automatic threshold segmentation result is stable and reliable, greatly reduces manual intervention.

The invention discloses a visual prompt method and device of a large visual language model based on a self-attention mechanism, and relates to the field of multi-modal large language models. The method solves the problems that the expression form of hierarchical attention in a large model adopted by an existing visual prompt method can generate a lot of noise, and a key attention area provided by an attention positioning method is relatively fuzzy and still needs to be segmented by an auxiliary component, and comprises the following steps of: inputting an original image and an instruction into a large visual language model; extracting attention layers from the middle layer of the model, and combining a plurality of key attention heads of a plurality of attention layers to form an attention image; gaussian filtering is adopted to optimize the attention image area noise; the continuous attention images are converted into Boolean image region masks through an automatic threshold binarization method, and the Boolean image region masks are used for extracting regions related to answers; and fusing and aligning the extracted areas related to the answers with the original image, and feeding back to the large visual language model to generate and output a reasoning result.