672results about How to "Reduce missed detection rate" patented technology

The invention provides a semiconductor chip defect automatic detection method and system based on hyperspectral imaging, and relates to the technical field of chip detection, and the method comprises the steps: collecting a chip hyperspectral image, carrying out the image enhancement and illumination non-uniformity correction through an improved neuromorphic adaptive processing network and a dual-path diffusion model, and carrying out the detection of the hyperspectral image. Extracting multi-level features by using a dynamic mixed feature enhancement network based on neural architecture search, fusing the features through a cross-layer attention mechanism, generating an initial defect detection result by using a multi-branch expert decision-making module and an uncertainty dynamic integration strategy, constructing a space-time knowledge inference graph, and determining a defect detection result; and performing reliability evaluation and optimization on the initial defect detection result in combination with the form similarity, the spatial position relationship and the process parameter influence model to obtain a final defect detection result.

The invention discloses an industrial product quality detection method and system based on machine vision, and relates to the technical field of quality detection.During operation of the system, through an industrial camera and an optimized light source layout, high-resolution image acquisition is performed on electronic components in real time by adopting multi-angle shooting, a series of optimization processing is performed on the acquired images, and the quality of the electronic components is detected. The method comprises the following steps: extracting shape, texture and color features according to geometric characteristics of an electronic component to form key parameters for judging defects, identifying potential defects of the electronic component by using a deep learning model algorithm, calculating to obtain a quality evaluation index Q, and calculating the quality evaluation index Q according to calculation results of feature extraction and defect detection. And comprehensively evaluating the quality of each component, comparing the quality evaluation index Q with a first preset threshold value M and a second preset threshold value N to obtain a quality evaluation grade, feeding unqualified detection information back to a production line, and removing unqualified components.

The invention provides a methane leakage anomaly detection system and method based on dual-light fusion, and belongs to the field of industrial safety detection. According to the system, a double-light-path holder type laser detector is arranged in a monitoring area, and methane concentration and image data are synchronously acquired through a 1653nm near-infrared laser module and a visible light image module. Time features (mean values, trends and the like) are extracted from the concentration data through time sequence analysis, and spatial features (edges, textures and the like) are extracted from the image data through deep learning; the system fuses time and space features to generate a comprehensive feature vector, reduces the calculation complexity through a dimension reduction technology, and retains core features at the same time. And constructing an anomaly detection model based on an unsupervised learning algorithm, identifying anomaly features in real time, and triggering an alarm. The system realizes real-time and accurate detection of methane leakage, and enhances the reliability and efficiency of industrial safety.

The invention discloses a segmentation method for rail defect detection based on improved YOLOv10 and SETR, and the method comprises the steps: S1, collecting image data, carrying out the enhancement processing of the image data, carrying out the segmentation processing, and making a data set; s2, an improved YOLOV10 algorithm model is built and trained; s3, using the improved YOLOV10 model weight to detect image data to be detected, judging whether defects exist or not, if the defects are detected, storing detection frame information of the defects, and outputting a target detection frame and confidence; and S4, transmitting the detection frame information as prompt information into the SETR model, segmenting the target by adopting the SETR model to obtain a segmentation result, and outputting a mask pattern. The improved YOLOv10 algorithm and the SETR algorithm are adopted to detect the rail defects, the calculation complexity and the efficient detection speed are kept, the detection precision is improved, and the robustness of the model is improved.

The invention provides a remote sensing image target dynamic detection method based on a multi-kernel, and belongs to the field of remote sensing image detection.The method comprises the steps that a remote sensing image is collected, a YOLOv8 model is optimized, information extraction is conducted on the remote sensing image through a multi-kernel-based cross-stage feature fusion module in a backbone network of the optimized YOLOv8 model, and the remote sensing image is obtained; obtaining feature maps of different scales; performing dynamic up-sampling on the feature map by using a neck network, outputting an up-sampled feature map in response to the neck network, adding a dynamic detection head, and performing optimization and feature fusion on the up-sampled feature map by using an attention mechanism to obtain a fused feature map; and classifying the feature map to obtain a classified target detection result. According to the method, the problems of insufficient experimental effect on a multi-category data set, large number of small targets, large scale change and dense distribution of remote sensing image targets and detection conditions under a complex background in the prior art are solved.

The invention relates to the technical field of intelligent inspection, in particular to an intelligent inspection staring system fusing large and small models, which is applied to real-time monitoring and alarming in a communication pipeline construction scene, and aims to solve the problems of high false alarm rate, difficulty in information retrieval and low report generation efficiency in an existing communication pipeline construction monitoring system. According to the invention, by combining a cooperative working mechanism of a lightweight small model and a multi-modal large model, real-time detection and tracking of a construction site target, deep analysis of an abnormal event, intelligent question and answer of video content and automatic generation of a construction auditing report are realized, so that the intelligent level of communication pipeline construction monitoring is improved.

The invention relates to the technical field of battery detection, and particularly provides an anomaly detection method and device for a battery module, and the method comprises the steps: constructing a digital twin model which comprises a battery pack model and a charging system model; performing fault injection on the digital twin model; performing simulation operation on the digital twin model after fault injection to generate a simulation operation data set; constructing a deep neural network, and training the deep neural network based on the simulation operation data set; acquiring battery data of a to-be-detected battery module, and inputting the battery data into the trained deep neural network to obtain an output result; and judging whether the to-be-detected battery module is abnormal or not according to the simulation operation data set and the output result. Therefore, fault injection and simulation operation are carried out on the basis of the digital twinborn model, abundant training data are provided for the deep neural network, the spatial relationship between the battery cells is considered, the accuracy of anomaly detection can be improved, and the false detection rate and the omission ratio can be reduced.

The invention discloses an abnormal defect detection method and system applied to PCB intelligent welding, and relates to the technical field of PCB welding detection, and the method comprises the following steps: carrying out the image collection of a whole PCB through a high-definition camera, and recognizing and positioning all welding spots on the PCB through an image segmentation technology; and each welding spot is detected according to the initially set expected resolution, and after the welding spots with obvious welding problems are recognized, the recognized welding spots are screened out. According to the method, the welding spots are accurately positioned through the image segmentation technology, the detection efficiency is rapidly screened and improved by using the initial resolution, and obvious non-pseudo-soldering welding spots and potential pseudo-soldering welding spots are efficiently classified in combination with a deep learning model. Aiming at potential cold solder joints, the resolution is dynamically adjusted, high-resolution fine detection is performed, the cold solder problem is accurately identified, the detection accuracy and precision are remarkably improved, the omission ratio and the misjudgment ratio are reduced, and reliable technical support is provided for PCB intelligent welding quality control.

The invention discloses a PCB surface defect detection method and system, an electronic device and a medium, and relates to the technical field of defect detection, the connection condition caused by excessive tin soldering of a tin bridge or adjacent welding spots can be automatically identified by judging the connection state between the welding spots, an overall detection instruction is triggered, early detection is facilitated, connectivity defects are avoided, and the detection efficiency is improved. And the welding spot connection reliability is improved. The image is divided into a plurality of image blocks, the texture difference data of each image block is obtained, the texture abnormal state of the surface of the PCB can be finely analyzed by calculating the texture deviation coefficient, the subtle texture change in different areas can be identified by the block analysis method, and surface defects such as scratches and bubbles on the surface of the PCB can be accurately detected. And in combination with the texture deviation coefficient and the geometric features of the welding spots, unified normalization fitting is performed on the defect features to generate the defect recognition index, so that the defect features of different types can be captured, and the robustness and accuracy of the model can be improved.

The invention relates to the technical field of new-generation information, in particular to a braid defect detection method and system based on artificial intelligence. The method comprises the following steps: acquiring dynamic images of the front and back surfaces of a braid through an industrial camera, adjusting exposure time and light source brightness, and generating an acquisition image; performing segmentation and enhancement processing on the acquired image, extracting an image feature sequence of a defect area of the braid, and generating standardized image data; inputting the standardized image data into a deep learning model, identifying a defect region, and outputting a defect position, confidence and area; and generating an NG signal based on the defect parameters, transmitting the NG signal to a PLC (Programmable Logic Controller) to control a labeling machine to mark a defect area, storing defect data and generating a visual analysis report. Through combination of high-precision image acquisition and a deep learning algorithm, accurate identification and real-time feedback of complex defects on the surface of the braid are realized, the false alarm rate and the omission ratio are remarkably reduced, and the detection efficiency and the braid production quality are improved.

The invention discloses an intelligent gas pipeline inspection method based on an unmanned vehicle, and relates to the technical field of unmanned detection, and the method comprises the steps: making an inspection plan, and planning an inspection route; establishing a precise map to realize autonomous navigation and positioning, and performing autonomous inspection according to a planned inspection route; an environment monitoring sensor module carried on an inspection unmanned vehicle is used for monitoring the operation environment of a gas pipeline, and when an environment air index is detected to be abnormal, an ultrasonic sensor array is used for identifying a sound wave signal generated by gas pipeline leakage, positioning a gas leakage point and recording and reporting early warning information and on-site conditions to a remote monitoring station; meanwhile, sound-light alarm information is generated; data of sensors along the gas pipeline and in the gas pipeline are read, an inspection video is uploaded to a server, and a special inspection task is completed through remote control. According to the invention, autonomous inspection can be realized, gas leakage can be accurately identified, a gas leakage point can be positioned, and the harm of gas leakage accidents can be reduced.

The invention relates to the technical field of crack detection, and discloses an aluminum alloy component crack detection method and device.The method comprises the steps that an aluminum alloy component is subjected to CT scanning and preprocessing, and a preprocessed CT image is obtained; performing crack judgment through an integrated classification model of a bilinear convolutional neural network and a support vector machine classifier to obtain an image which is judged to have cracks; performing crack width transformation to generate a crack width graph; carrying out region growing algorithm search to obtain a complete crack region and a corresponding crack quantitative parameter set; performing vibration analysis on the aluminum alloy component to obtain vibration characteristic data; a stress intensity factor is calculated by using a continuous damage mechanical model, and fatigue crack propagation analysis is performed to obtain residual life prediction data, so that the accuracy of crack identification is improved, accurate extraction of crack features is realized, and reliable residual life prediction is performed on the basis.

The invention discloses a photovoltaic cell hot spot intelligent detection method fusing multi-modal data. The method comprises the following steps: S1, collecting multi-modal data of a photovoltaic cell; s2, carrying out preprocessing on the multi-modal data; s3, constructing a photovoltaic cell sample data set; s4, extracting features of the multi-modal data; s5, fusing the multi-modal features to generate a comprehensive feature vector; s6, training and optimizing a dynamic hot spot detection model based on the space-time convolutional network; s7, identifying a hot spot area in the photovoltaic cell by using the model; s8, positioning and outputting a hot spot detection result; and S9, optimizing the detection model in real time in combination with a dynamic adaptive algorithm. According to the method, the multi-modal data fusion, the space-time convolutional network and the dynamic adaptive optimization algorithm are introduced, so that the hot spots in the photovoltaic cell can be detected and positioned more accurately and quickly, the false detection rate in a complex environment is effectively reduced, and the operation efficiency and reliability of a photovoltaic cell system are remarkably improved.

The invention belongs to the technical field of disease source prevention and control, and particularly relates to an animal skin harmful factor detection method and system for animal disease source prevention and control, and the system comprises a multi-mode sensor detection module which can respectively detect microorganisms, chemical residues and other harmful factors on animal skins; the intelligent analysis and machine learning module is used for fusing the multi-modal data acquired by the sensor, and classifying, identifying and analyzing various data through a machine learning algorithm; a detection model is continuously optimized through machine learning, and the recognition precision of pathogens and harmful factors is gradually improved; and the Internet of Things real-time monitoring and automatic alarm module uploads detection data of each sensor to a cloud platform in real time through the Internet of Things technology, so that managers and technicians can monitor the state of the leather at any time. According to the invention, real-time and automatic detection of animal skin harmful factors is realized, improvement of the overall effect of animal disease source prevention and control is facilitated, and the method has a wide application prospect.

The invention discloses a point cloud image data fusion method, system and device based on an improved DBSCAN clustering algorithm and a storage medium, and belongs to the field of multi-sensor data fusion, the method uses an acquisition system composed of a laser radar and a camera to complete point cloud and image shooting and acquisition of a detected target scene, and the point cloud and image shooting and acquisition have different characteristics; the sensor fusion mode is used for target detection, so that advantage complementation can be realized; an improved DBSCAN algorithm is used for completing detection target point cloud clustering and generating a three-dimensional bounding box, finally, a point cloud cluster bounding box model is projected to a corresponding image pixel position, fusion of image data and point cloud data is completed, the DBSCAN point cloud clustering algorithm is improved, automatic calculation of a minimum neighborhood and an initial radius value is achieved, and finally, point cloud detection is completed. And the efficiency of detecting the target point cloud clustering is improved, so that the robustness and the accuracy of the detected target are improved, and the omission ratio and the false detection rate of target detection under the condition that the illumination condition is insufficient are reduced.

The invention discloses a defect detection sensor and a defect detection method based on a quantum technology, and relates to the technical field of defect detection.The defect detection sensor comprises a central controller, an orthogonal differential detection unit, a quantum magnetometer and a magnetic shielding cylinder; the orthogonal differential detection unit is used for collecting a first magnetic field change signal on the surface of a preset area of the preset part, transmitting the first magnetic field change signal to the quantum magnetometer and applying the first magnetic field change signal to alkali metal atoms of the quantum magnetometer; the quantum magnetometer is used for obtaining a second magnetic field change signal and sending the second magnetic field change signal to the central controller; and the central controller is used for analyzing the second magnetic field change signal and determining whether the preset area of the preset part has defects or not. According to the invention, the sensitivity and accuracy of defect detection can be improved.

The invention provides an infrared weak and small target detection method and system, electronic equipment and a storage medium, and belongs to the technical field of target detection, and the method comprises the steps: improving a YOLOv8 model for the features of an infrared weak and small target image; and performing target detection on the infrared weak and small target image by using the improved YOLOv8 model. According to the invention, a stronger infrared weak and small target detection capability is provided for the YOLOv8 model, and the problem of target detection precision bottleneck of a traditional model in a complex scene is effectively solved.

The invention relates to the technical field of detectors, in particular to a detector for detecting the interior of a buried polyethylene gas pipe and a detection method thereof.The detector comprises a body of a capsule-shaped structure on the whole, and the body comprises a speed control mechanism, an acoustic detection mechanism, an auxiliary movement mechanism and a communication mechanism which are sequentially arranged in the axial direction; the acoustic detection mechanism is used for collecting sound signals in a pipeline, the auxiliary movement mechanism is used for making contact with the inner wall of the pipeline and sealing the inner wall of the pipeline, so that pressure difference is formed between the front portion and the rear portion of the body, and the body is driven to move in the pipeline. The opening degree of the air inlet is controlled in cooperation with the opening degree adjusting mechanism, the displacement speed of the body in the pipeline is controlled, sound in the pipeline is collected through the acoustic detection mechanism, rapid pipeline detection is achieved, the detection efficiency of the buried pipeline is improved, the omission ratio is reduced, and the situation that existing manual detection wastes time and labor is avoided, and the omission ratio is high is avoided.

The invention relates to a bridge construction quality detection method, system and platform supported by machine vision, and relates to the field of quality detection, and the method comprises the steps: carrying out the first-order crack recognition, and obtaining a target region; collecting a target region image, performing image salient region identification to obtain an image saliency and an image salient region, and dividing a salient region image of the image salient region; configuring an identification node number according to the image saliency, and performing second-order crack identification on the salient region image to obtain crack feature information; and configuring a bridge deformation detection range according to the crack feature information and the image saliency for deformation detection to obtain a deformation detection result, and combining the crack feature information as a bridge quality detection result. The technical problems that a traditional bridge construction quality detection method cannot efficiently recognize bridge crack defects and deformation defects, the missed detection probability is large, the detection precision is low, and the detection efficiency is low can be solved; and the efficiency, the accuracy and the reliability of bridge quality detection can be remarkably improved.

The invention discloses a chromosome segmentation method, system and device based on chromosome clusters and a medium, and belongs to the technical field of biomedicine and artificial intelligence in bioinformatics, and the segmentation method comprises the steps: carrying out the labeling processing of a chromosome original image, sequentially constructing a chromosome cluster recognition data set for different tasks, and training a chromosome cluster recognition network; constructing a chromosome cluster classification data set, and training a chromosome cluster classification network; constructing a chromosome cluster segmentation data set, and training a chromosome cluster segmentation network; inputting a chromosome original image, and performing reasoning by using the trained chromosome cluster recognition network, chromosome cluster classification network and chromosome cluster segmentation network in sequence to obtain a chromosome segmentation result; according to the invention, the chromosome can be automatically and accurately identified and segmented from the observation image; and by using the architecture based on the chromosome cluster, the probability of missing detection of the chromosome is reduced, and the downward compatibility of the network to the image resolution is high.

The invention discloses a weld DR image defect intelligent identification method based on local-global efficient modeling improved YOLOv5. According to the method, an iRMBConv efficient feature extraction module is designed, and the module combines the technical advantages of dynamic global modeling and static local information fusion. In the Backbone network, the iRMBConv module can fully extract the multi-scale defect features of the weld DR image, and the recognition precision of the defects of different scales can be improved. Besides, in the Neck network, a context sensing module CSM is designed, the characteristics of different receptive fields are obtained by adopting expansion convolution with different expansion rates, the characteristic information of the tiny defects in the weld DR image is effectively enhanced, and then the recognition precision of the model on the tiny defects is improved. The intelligent identification model has high identification precision and good robustness, is suitable for the industrial fields of aviation, aerospace, nuclear industry, special equipment and the like, and promotes the application and deployment of the weld DR image defect intelligent identification technology.

The invention relates to a primer group and a kit for detecting Fabry disease GLA gene mutation and application of the primer group and the kit. The primer group comprises one or more of a GLA-F1 / R1 primer pair, a GLA-F2 / R2 primer pair, a GLA-F3 / R3 primer pair, a GLA-F4 / R4 primer pair, a GLA-F5 / R5 primer pair, a GLA-F6 / R6 primer pair and a GLA-F7 / R7 primer pair. The kit further comprises a GLA-Gap primer. Based on a specific combination of long-fragment PCR amplification and third-generation high-throughput sequencing, the method can be used for simultaneously detecting various mutations related to the Fabry disease pathogenic gene GLA of a plurality of samples in a high-specificity, accurate and rapid manner.

The invention belongs to the technical field of tunnel monitoring, and particularly relates to a tunnel multi-period change condition monitoring method based on linear array image data, which comprises the following steps: step 1, image registration; step 2, image change detection; and step 3, marking a change area. According to the method, image registration is carried out on the obtained two-stage images, then the change condition of each pixel is judged by using the SSIM index and the threshold value, the change area is obtained, and finally, labeling and re-checking of the change area are realized manually, so that timely monitoring of the long-term change condition is realized. Compared with the prior art, multi-stage image data of the same area are used for contrastive analysis, pixel-level detection of tunnel images can be achieved, small changes of a tunnel can be sensitively recognized, new small diseases can be found in time, the omission ratio is low, the development and change conditions of existing diseases can be continuously monitored, the conditions of equipment and facilities in the tunnel can be updated, and the efficiency is improved. And the method has good consistency for tunnels under different conditions.

The invention discloses an AI identification method and system for a millimeter wave image overlapping target based on a three-branch network, and a model training method, and the method comprises the steps: carrying out the cooperative work of three independent branch networks: an analysis branch network, an aggregation branch network, and a prediction boundary branch network; the problem of security inspection target detection in millimeter wave images with low resolution, mutual interference or mutual overlapping is solved. The analysis branch network stores detail information in the high-resolution feature map; the aggregation branch network realizes feature position offset alignment of high-resolution and low-resolution feature maps through a recursion fusion module; and the prediction boundary branch network extracts high-frequency features, and enhances perception and prediction of the target edge. The multi-branch network structure effectively realizes image segmentation and identification through fusion among branches. While the segmentation performance of the model is improved, the model significantly reduces the calculation complexity, reduces the detection error and missed detection probability in the millimeter wave image, and finally achieves the precise detection of overlapped objects.

The invention relates to a shield tunneling machine cutter fault diagnosis method, device and system, a program product and a medium. The shield tunneling machine tool fault diagnosis method comprises the steps that working state data of a shield tunneling machine are acquired, and the working state data comprise tool changing records in the tunneling process of the shield tunneling machine and equipment monitoring data and tool monitoring data in the operation process of the shield tunneling machine; performing feature extraction on the working state data to obtain a fault diagnosis vector; and processing the fault diagnosis vector by adopting a hybrid model, and determining the fault category of the shield tunneling machine cutter. According to the method and the device, the monthly, quarterly or annual carbon emission of the discrete manufacturing enterprise can be quickly, efficiently and accurately predicted based on the order information.

The invention discloses a test method and system for a radio frequency operation electrode. The invention relates to the technical field of medical instruments. The input layer comprises a proportional neuron P, an integral neuron I and a differential neuron D; and receiving the independent variable X and converting the independent variable X into PID parameters P including a proportionality coefficient Kp, an integral coefficient Ki and a differential coefficient Kd. The fuzzy reasoning layer comprises five fuzzy neurons which all use a bell-shaped function to calculate respective fuzzy values of a proportionality coefficient Kp, an integral coefficient Ki and a differential coefficient Kd, and then five reasoning factors IF are generated based on a fuzzy rule set. The output layer inputs the inference factor IF into a target function F; through the fuzzy PID neural network, the non-linear relation between independent variables (equipment types, load resistance values and test circuit connection modes) and dependent variables (high-frequency leakage current values) in the radiofrequency operation electrode detection process can be captured. Through the capability, the detection result is more in line with the actual situation, and the detection accuracy is improved.

The invention discloses a target detection method, system and device based on improved YOLOv5s and a storage medium, and the method comprises the steps: providing a picture data set, and manually marking a detection target for a picture in the data set; constructing a YOLOv5s network model, adding a GAM attention mechanism in a feature fusion network, and using an SIoU position loss function by a detection head; a YOLOv5s network model is trained and tested; and inputting a picture of the unlabeled detection target to obtain a detection target result. According to the method, the GAM attention mechanism is added in the feature fusion network, so that the model is more focused on features beneficial to target detection, and the detection accuracy is improved. Secondly, a position loss function is replaced by SIoU, so that the convergence speed of the model and the reasoning accuracy are improved; the improved YOLOv5s is used for target detection, so that the omission ratio and the false detection rate of target detection are reduced to a certain extent.

The invention relates to a traffic small target detection method and device for a high-speed driving vehicle, equipment and a medium, and relates to the field of image processing, and the method comprises the steps: inputting a traffic scene image frame into a second traffic small target detection model which is trained to a convergence state, and carrying out the detection of a traffic scene image frame in a plurality of multi-layer stacked recursive Star Block modules of a StarNet lightweight network, performing element-by-element multiplication on image features corresponding to the traffic scene image frames by adopting star operation to exponentially increase implicit feature dimensions in the traffic scene image frames so as to determine high-dimensional image features of the traffic scene image frames; and inputting the high-dimensional image features into a detection head network in a traffic small target detection model to obtain a plurality of traffic small targets in the traffic scene image frame so as to complete traffic small target detection of the high-speed driving vehicle. According to the method, the small traffic target can be accurately and quickly detected in a traffic scene in high-speed driving, and higher target detection performance can be realized in a resource-limited environment such as high-speed driving vehicles.

The invention provides an underwater target echo detection method and system based on amplitude fluctuation characteristics, and the method comprises the steps: S1, building a target detection model according to a preset rule; s2, acquiring an echo image, performing sliding window processing on waveform data in the echo image, and establishing a probability density function model; s3, calculating a likelihood ratio function of the waveform data according to the target detection model and the probability density function model; s4, calculating a threshold value according to the probability density function model and the likelihood ratio function; and S5, updating the echo image by using a threshold value as a criterion to obtain a target detection result. According to the invention, through constant false alarm rate detection and the Neyman-Pearson criterion, a dynamic threshold value detection method which fluctuates along with the target intensity and changes along with the background is provided, the echo image is detected, and a new echo image is formed.

The invention discloses a road surface disaster detection method and system, electronic equipment and a storage medium, and relates to the technical field of intelligent traffic management and computer vision, and the method comprises the steps: collecting image data containing a plurality of road surface disaster types, carrying out the data marking in each image data, and carrying out the data marking; a data sample is constructed through each piece of image data subjected to data annotation; training the improved YOLOv10 model by using the data sample until a loss function of the improved model reaches a preset end condition, and determining the improved model of which the loss function reaches the preset end condition as a disaster detection model; acquiring a to-be-detected image of a to-be-detected road surface, and inputting the to-be-detected image into the disaster detection model for processing to obtain a disaster detection result of the to-be-detected road surface, the disaster detection result including a disaster type and a disaster position; by combining the deep learning technology and optimization algorithm design, the problem that the existing method is insufficient in detection precision, robustness and applicability is solved.