243results about How to "Reduce false detection rate" patented technology

The invention discloses a PCB production line defect detection analysis method and system based on an identification model, particularly relates to the technical field of PCB quality detection, and is used for solving the problems of low PCB defect detection precision and high omission ratio in the prior art. The method comprises the following steps: scanning the whole surface of a PCB to generate a thickness distribution diagram, identifying a thickness non-uniform region by adopting a threshold segmentation algorithm, and marking a key detection region; potential defects irrelevant to process fluctuation are identified through historical production data analysis; and adjusting the polarization angle of the light source to analyze the reflectivity difference, and identifying the hidden defect area in combination with the microstructure of the material. The union set of the potential defect area and the hidden defect area is marked as the suspected incomplete area, and the influence of the suspected incomplete area on the performance of the PCB is analyzed according to the quality standard, so that the detection precision and comprehensiveness are improved, and the high-quality control of the PCB is realized.

The invention relates to the technical field of braid quality detection in industrial production, in particular to a defect data processing query method and system for braid visual inspection. The method comprises the steps of obtaining braid image data, performing enhancement processing on an image based on a dynamic illumination compensation algorithm, and generating a high-quality image in combination with material characteristics; inputting the enhanced image data into a deep learning defect detection model, detecting and classifying defect areas in the image, and updating the model weight based on defect feature parameters; performing multi-dimensional indexing processing on the detected defect feature data to generate a defect distribution database supporting real-time query; and the defect distribution database and the production process parameter sequence are combined, the causal relationship of defect occurrence is analyzed, process optimization parameters are generated and fed back to the detection module, and self-adaptive updating of model parameters is completed. According to the invention, high precision, high efficiency and closed-loop optimization of braid defect detection are realized, and intelligent support is provided for industrial production quality control.

The invention relates to the technical field of assembly welding, and particularly discloses an assembly welding positioning method and device for automobile parts. A welding state image between a target hush pipe and a target partition plate is collected through a camera; carrying out welding state mask segmentation on the welding state image by using an image identification and analysis algorithm based on computer vision, and then carrying out welding state feature extraction on the segmented welding detection area state mask image; and according to the expression obtained through neighborhood importance optimization of the extracted welding detection state characteristics, intelligent detection on whether defects exist in the welding state between the target hush pipe and the target partition plate or not is achieved. In this way, tiny defects such as tiny cracks and internal pores can be captured, and therefore the accuracy of defect recognition is improved. Moreover, the detection speed is greatly improved, the consistency and stability can be maintained, and the probability of missing detection or false detection is reduced, so that each welding point is ensured to be subjected to strict quality control.

The invention provides a forestry pest monitoring method and system based on machine learning, and the method comprises the steps: carrying out the principal component analysis of a forestry pest image, obtaining a plurality of recognition indexes of the pest of a target forestry region, determining an effective feature vector when the image recognition of the forestry pest is carried out according to all recognition indexes, and carrying out the recognition of the effective feature vector. Determining a first-level correlation feature of the pest image in the target forestry region through the effective feature vector; extracting a plurality of environmental influence factors from the monitoring image, and determining secondary correlation features of the pest image in the target forestry area according to all the environmental influence factors; carrying out interactive fusion on the first-level correlation features and the second-level correlation features to obtain multi-level distribution features for carrying out image recognition on the diseases and insect pests; and identifying the current pest and disease distribution of the target forestry area based on the multi-level distribution characteristics through a pest and disease identification model. By adopting the scheme of the application, diversified monitoring of the forestry diseases and insect pests can be realized under a complex background, so that the false detection rate of the forestry diseases and insect pests is reduced.

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 an identification method and system based on multi-sensor information fusion. According to the method, firstly, a one-dimensional recognition framework and a two-dimensional recognition framework are constructed, and sensor detection results and reliability thereof are described respectively; grouping is carried out according to sensor feature information, calculation is simplified, and the information fusion difficulty is reduced. And then the reliability of the sensor is evaluated by adopting an AHP-TOPSIS method, and evidence is corrected. And finally, integrating each group of evidences by applying a D-S evidence theory fusion rule to generate a final fusion detection result. The method effectively fuses multi-sensor data, improves decision-making efficiency, and is suitable for intelligent identification tasks in a complex environment.

The invention discloses an industrial defect detection and positioning method and system based on frequency domain enhancement, relates to the field of computer vision and image processing, can discover abnormal modes in an industrial image from a plurality of perspectives, and also has a relatively high reasoning speed. The method comprises the following steps: processing an input image through a high-pass filter to obtain a high-frequency image; inputting the original image and the high-frequency image into an improved U-Net network integrated with a double-domain feature selection module; image reconstruction is carried out in a spatial domain and a frequency domain at the same time; an anomaly score and a localization map are calculated based on the reconstruction result. According to the invention, by combining spatial domain and frequency domain analysis, comprehensive characterization of local pixel change and global frequency mode of the image is realized, and the accuracy of anomaly detection and positioning is improved. The method achieves the optimal performance on a plurality of real industrial scene data sets. The method is suitable for abnormal detection and abnormal area positioning of industrial product images.

The invention relates to the field of ocean internal solitary wave automatic detection, and discloses an internal solitary wave detection method based on sentinel No.3 satellite altimeter data, which comprises the following steps: acquiring sentinel No.3 satellite altimeter data of a sea area to be detected, and preprocessing the data; sequentially extracting a posterior scattering coefficient of each coordinate point of the Ku wave band and the C wave band, and response characteristic parameters SSHA and SWH; sequentially calculating the difference mean square slopes of the posterior scattering coefficients of the Ku wave band and the C wave band; the response characteristic parameters are smoothed twice, and variables # imgabs0 # and # imgabs1 # are obtained; and respectively calculating a differential mean square slope and a local standard deviation of variables # imgabs2 # and # imgabs3 #, and extracting data fluctuation characteristics by setting a threshold value so as to realize internal solitary wave detection. The method disclosed by the invention has higher accuracy and lower false detection rate, and still has a good detection effect under the condition that the propagation direction of the inner solitary wave is greatly different from the running direction of the satellite.

The invention relates to a woolen sweater defect detection method. The method is characterized by comprising the following four main steps: a data preprocessing step, a model construction step, a model training step and a defect detection step. Wherein in the model construction step, strategies such as an attention mechanism and a network structure optimization are introduced, so that the identification capability of the model on woolen sweater defects is improved; in the detection process, automatic positioning and classification of defect areas are realized through a series of key functions. The method is high in detection efficiency, low in comprehensive detection cost and high in detection accuracy.

The invention discloses a pointer instrument detection and identification reading method based on improved YOLOv8n, and the method comprises the steps: obtaining the image data of a pointer instrument, and sequentially carrying out the data expansion and dial marking, and obtaining a first marking data set; performing optimization adjustment based on partial convolution modules in a backbone network and a neck network of the YOLOv8n network structure and a convolution module in a head network to obtain an improved YOLOv8n network model; training the improved YOLOv8n network model based on the first annotation data set to obtain a dial region detection model; inputting the first annotation data set into a dial region detection model to obtain a dial data set; performing secondary annotation based on the dial data set to obtain a second annotation data set, and training the dial area detection model to obtain a target frame detection model; inputting the to-be-detected instrument image into the target frame detection model to obtain a target frame; and obtaining the pointer reading of the to-be-measured instrument based on the target frame. And the reading identification efficiency and accuracy of the pointer type instrument are improved.

The invention discloses a bridge crack detection method based on image recognition, and belongs to the technical field of image recognition, and the method comprises the steps: firstly, dividing a bridge image into three single-color channels of red, green and blue, carrying out the clustering and scanning of each spectral image, and precisely positioning a suspected crack point; a red channel weight matrix, a green channel weight matrix and a blue channel weight matrix are constructed by calculating a channel contrast value and a region track distortion value, and then gray processing and brightness adjustment are performed on a bridge image to enhance a crack region. Then, texture enhancement is carried out on the adjusted images through the red channel weight matrix, the green channel weight matrix and the blue channel weight matrix, and three texture-enhanced images are generated; and finally, processing the enhanced image by using a three-channel crack identification neural network to obtain the bridge crack, thereby improving the accuracy of crack detection.

The invention discloses a coal mine shaft crack identification method in a non-illumination environment based on an optical fiber sensing grating, and belongs to the field of coal mine shaft disease detection. According to the method, distributed optical fibers form an optical fiber sensing grating, the optical fiber sensing grating is fixed on the surface of a well wall, an optical fiber demodulator is used for collecting strain along the optical fibers, a blank blind area of the optical fiber sensing grating is filled through an interpolation algorithm, and a strain field image data set of the well wall is obtained; and a CCD industrial camera is matched to shoot a well wall crack image sample at the optical fiber sensing grating, binarization processing is carried out, and a well wall crack image data set is established. And establishing a well wall crack identification model based on deep learning, introducing two parallel feature extraction branches, respectively receiving an optical fiber strain field image data set and a well wall crack image data set, and performing feature extraction and fusion of the two different types of data sets through training. Because the strain data is sensed through the optical fiber, the illumination environment is not needed, and after the model training is completed, the identification and early warning of the well wall crack in the illumination-free environment can be realized only by inputting the optical fiber strain data acquired in real time into the trained model.

The invention discloses an optical remote sensing image saliency target detection model and detection method, and belongs to the technical field of target detection. The saliency target detection model comprises a PVT backbone network module, an edge information extraction and fusion module BG, a feature enhancement module HAM, two space attention modules, a feature interaction module KTM and a feature fusion module. According to the invention, by introducing an edge detection module (BG) and a feature enhancement module (HAM), the target detection precision under a complex background is effectively improved. Compared with the existing CNN-based method, the method provided by the invention can better process targets with different scales and direction changes, reduces the probability of missing detection and false detection, and enables saliency target detection to be more complete and accurate.

The invention discloses a detection and tracking method and device for dense pedestrians, relates to the technical field of computer vision, and can solve the problem of low tracking accuracy during real-time detection and tracking in a scene with extremely dense pedestrians in the prior art. According to the specific technical scheme, firstly, a dense pedestrian detection data set and a tracking data set are obtained; an alpha-EIoU loss, a GAM attention mechanism and a Swin Transform module are used for improving a YOLOv5s algorithm, and a detection model is obtained; training and optimizing the detection model to obtain a target detection model; a ShuffleNetV2 module is introduced to improve a DeepSORT algorithm, and then training is performed to obtain a target tracking model; and detecting and tracking the pedestrian target video according to the target detection model and the target tracking model to obtain an experimental result of detection and tracking. The method is used for detecting and tracking dense pedestrians.

The invention discloses a tunnel fire detection and simulation method and system driven by video data, belongs to the technical field of computer vision and machine learning, and solves the problem that the current tunnel fire target detection algorithm is easy to cause insufficient distinguishing capability of vehicles and flames in a complex multi-target scene environment. The method comprises the following steps: preprocessing an obtained original data set to obtain a target data set to train an improved YOLOv8 neural network model, and carrying out target detection on a to-be-detected tunnel fire scene disaster image to obtain a central coordinate of each preliminary disaster object target for correction; after correction, a monocular vision positioning method is adopted to carry out position calibration and distinguishing on the corrected disaster situation object target, and then real-time scene twinborn modeling is carried out, so that a tunnel fire scene twinborn model is obtained and dynamically updated; and performing flame spread analysis on the tunnel fire scene based on the tunnel fire scene twinborn model, and realizing dynamic updating in un ity. The method is used for tunnel fire detection and simulation driven by video data.

The invention provides an algae target detection method, system and device based on deep learning, and relates to the field of computer vision environment monitoring, and the method mainly comprises the steps: collecting the image data of a water body through an image sensing device; splitting and preprocessing the image data according to a preset size; performing small target splitting and data enhancement on the training image data; based on the training image data, training the optimized YOLO target detection model; and performing target detection on the detected image data through the trained YOLO target detection model to obtain a detection result. According to the scheme, by introducing and optimizing a YOLO series algorithm, the automation degree and the detection precision of algae detection can be remarkably improved; according to the scheme, algae target detection and classification can be accurately and efficiently carried out in complex water backgrounds and various algae environments, and the false detection rate can be effectively reduced; the scheme is suitable for real-time monitoring and analysis scenes of a large-scale and complex water body environment.

The invention discloses a tailing dry beach automatic detection and early warning method based on multi-sensor image combined processing, and belongs to the field of image data processing. According to the method, an unmanned aerial vehicle carrying an RGB camera, a multispectral camera and a laser radar is used for collecting RGB images, multispectral images and three-dimensional point cloud data, then various feature data are obtained, boundary information is obtained through fusion and a random forest classifier, then the length of a dry beach is calculated, and early warning is carried out. According to the method, the prediction accuracy and efficiency are improved, the advantages of various data are fully utilized, the tailings pond monitoring has higher reliability and flexibility, and meanwhile the method has the advantages of being good in random forest classification effect, high in discrimination capability, low in false drop rate and the like.

The invention relates to a powder spreading defect detection method based on deep learning image segmentation. According to the powder spreading defect detection method, data enhancement and training sample expansion are performed on the image data of the marking training set, more samples are generated from limited original data, the diversity of the data is increased, the problem of insufficient data volume is solved, and the generalization of an image segmentation network model is improved. A final image segmentation network model of lightweight deep learning is obtained through establishment of a primary image segmentation model, multi-round model training and training effect verification and evaluation, and defects such as insufficient powder spreading, fine defects (warping) and slender defects (scraper scratches) in the powder spreading process can be effectively detected by using the final image segmentation network model. And the whole process does not need to manually adjust parameters in real time due to scene change, so that the operation is simple and convenient, and the false detection rate is reduced. By using the powder spreading defect detection method based on deep learning, the reliability and accuracy of powder spreading defect detection are effectively improved.

The invention relates to the field of defect detection, and discloses a dumper carriage weld defect detection method and device and a storage medium, and the method comprises the steps: collecting dumper carriage weld defect images to construct an original data set; marking the original data set, and dividing the original data set according to a preset proportion; constructing an improved compartment welding seam detection model based on a YOLOv8n network structure; inputting the training set into the improved model and the original YOLOv8n model for training; and different degrees of salt and pepper noise are added in the original data set, and the detection performance of the original model and the improved model under the extreme noise condition is tested. According to the method, the problems of high missing detection rate and false detection rate of weld defect detection in the prior art are solved, so that the weld defect detection precision is improved, the calculation complexity of the YOLOv8n model is reduced, and the YOLOv8n model can be better deployed on industrial edge equipment.

The invention provides an automatic calibration method and system based on joint point searching of a total station and a camera, and the method comprises the steps: respectively building a two-dimensional coordinate system and a three-dimensional coordinate system with an industrial camera and the total station as original points, and extracting the two-dimensional positioning information of a to-be-monitored mark point from a two-dimensional image shot by the industrial camera; then calculating three-dimensional positioning information of point location data obtained by dotting relative to three-dimensional coordinates of the industrial camera by using an Euclidean distance formula, then performing information matching on the two-dimensional positioning information and the three-dimensional positioning information to obtain two-dimensional-three-dimensional positioning information pairing of the same mark point to be monitored, and then performing two-dimensional-three-dimensional positioning information pairing according to the two-dimensional-three-dimensional positioning information pairing. And solving a pose parameter of the industrial camera, back-projecting the three-dimensional positioning information to the two-dimensional image, calculating to obtain a back-projection error, and if the back-projection error is smaller than an error threshold value, converting the two-dimensional positioning information into a three-dimensional physical coordinate in a three-dimensional coordinate system according to the pose parameter, thereby completing two-dimensional-three-dimensional joint calibration.

The invention discloses a novel multi-scale Top-Hat infrared small target detection method. The method comprises the following steps: step S110, obtaining an original image matrix containing an infrared small target; step S120, respectively constructing annular structure elements under different scales; step S130, performing multi-scale Top-Hat transformation on the original image matrix by using the constructed annular structure elements to generate enhanced target matrixes under multiple scales; step S140, calculating a non-zero mean value of each enhanced target matrix, and screening out the enhanced target matrix with the maximum mean value; s150, in the enhanced target matrix with the maximum mean value, extracting a part higher than the mean value as a suspected target area; and S160, calculating each connected domain in the suspected target area, and screening the connected domains according to a preset small target pixel number, thereby realizing target detection. According to the technical scheme, compared with structural elements of a single scale, targets of different sizes can be more effectively captured.

The invention relates to the field of cable quality detection, in particular to a few-sample cable production surface defect detection method and device and a medium. The method comprises the following steps: firstly, loading a Resnet50 model pre-trained by an ImageNet data set, and migrating the universal feature extraction capability of the model learned from a big data set to the method; introducing a channel-space attention mechanism to enhance the perception capability of the model on the surface defect characteristics of the cable; performing structure and distribution modeling on the positive sample features through unsupervised learning, and constructing a positive sample feature library; in the detection stage, abnormal features are detected by comparing the difference between the test image features and the positive sample library, and then the detected abnormal feature heat map matrix is input into the self-adaptive threshold interception module to obtain a defect detection result. According to the method, the requirement for the data size is reduced, the cable defect detection requirement in a few-sample scene is met, and the false detection rate is effectively reduced based on the self-adaptive threshold interception method.

The invention discloses a distributed photovoltaic panel anomaly detection method under a high-altitude view angle based on a YOLOv8n model, and belongs to the technical field of target detection. The model comprises the following steps: acquiring a distributed photovoltaic panel image data set under a high-altitude view angle, and performing data enhancement and labeling; an improved YOLOv8n model is constructed, and the improvement comprises the steps that a C2fAT module is introduced into a backbone network, and the small target feature extraction capacity is enhanced; an SPPF module is replaced by an SPPF-LSKA module, and complex background interference is suppressed; an EMA attention mechanism is introduced into the neck network, and the multi-scale adaptive capacity is improved; optimizing a training process by adopting a WIoU v3 loss function; and training and optimizing the model by using the training set, and finally outputting a detection result through the test set. According to the invention, the problems of false detection and missing detection of the distributed photovoltaic panel in a high-altitude view angle are effectively solved, the detection precision of the distributed photovoltaic panel is further improved, and inspection personnel are helped to troubleshoot the photovoltaic panel in an abnormal state in the high-altitude view angle.

The invention relates to the technical field of aircraft maintenance project management, and discloses an aircraft defect accurate recording system and method, and the system comprises a data collection module, an image processing module, a feature processing module, a defect recognition module and a result output module. The data acquisition module comprises an image acquisition unit and an auxiliary information acquisition unit; the image processing module is used for preprocessing the surface image; the feature processing module is used for extracting target features; the target features are converted into feature vectors; an identification model is arranged in the defect identification module; the recognition model takes the feature vector as input, and takes the defect rate and the defect category as output; and the result output module is used for positioning a defect position and visually displaying and storing a defect analysis result according to the output result of the defect identification module and the corresponding feature vector. According to the method, defect feature analysis dimensions are diversified, defect information can be fully captured, the identification precision is high, and systematic intelligent management of aircraft defects can be realized.

The invention provides a telecommunication fraud detection method, and the method comprises the steps: obtaining attribute information and historical transfer information provided by a user, and inputting the attribute information and historical transfer information into a telecommunication fraud detection model which comprises a graph attention network and a classification neural network; then, attribute feature vectors are constructed according to the attribute information, a historical transfer relation graph is constructed according to the historical transfer information, and the historical transfer relation graph comprises historical transfer feature vectors; then, according to the attribute feature vector and a historical transfer relation graph, based on a graph attention network, extracting fusion transfer features of the user; and inputting the fused transfer features into a classification neural network to obtain a telecommunication fraud prediction result. According to the method, by analyzing the relevance between the attribute information provided by the user and the historical transfer information, whether the user relates to the telecommunication fraud or not is detected and analyzed from the overall perspective, and the accuracy and efficiency of telecommunication fraud detection are improved; meanwhile, the user information utilized by the method is provided by the user, so that the information acquisition difficulty is reduced.

The invention relates to an intrusion object detection method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring to-be-detected image data; performing multi-scale feature extraction on the to-be-detected image data by adopting a mixed attention mechanism through a multi-scale feature extraction layer to obtain a plurality of image data intermediate features of different scales; performing multi-direction expansion convolution processing on each image data intermediate feature through a feature enhancement layer to obtain a plurality of image data enhancement features of different scales; and through a detection layer, feature fusion is carried out on the image data enhancement features to obtain multi-scale fusion features, and intrusion object detection is carried out based on the multi-scale fusion features. By adopting the method, the false detection rate of intrusion detection of small-size intrusion objects can be reduced.

The invention discloses a life body feature separation method based on millimeter-wave radar signal enhancement, and the method comprises the following steps: obtaining an original radar signal, collected by a millimeter-wave radar, of a life body monitoring target, and carrying out the preprocessing of the original radar signal; six types of features are extracted from the preprocessed signals, and the preprocessed signals are divided into four scene types based on the six types of features; according to the divided scene categories, calling a corresponding signal processing model to enhance or reconstruct the preprocessed signal to obtain an enhanced radar signal; and performing signal quality evaluation on the enhanced radar signal, inputting the enhanced signal meeting the quality evaluation requirement into a variational mode decomposition module, and separating a respiration signal and a heartbeat signal of the life body monitoring target. According to the method, the millimeter wave radar vital sign signals in a complex scene are subjected to scene classification and adaptive calling of the model to enhance the signals, breathing and heartbeat are separated through variational mode decomposition, and the vital sign detection precision in a complex environment is improved.

The invention relates to a novel environment-friendly gas non-contact leakage detection method and system and electronic equipment. The method comprises the following steps: firstly, acquiring multi-spectral infrared image data, wherein the infrared image data comprises background information and leaked mixed gas information; secondly, calculating a gas index based on the multi-spectral infrared image data to obtain multiple groups of gas index data; and finally, preprocessing the multiple groups of gas index data, carrying out binarization classification by using a method based on an empirical model to obtain a binary image, and determining a target area of a mixed gas leakage position according to the pixel size in the binary image. Compared with the prior art, the method has the advantages that the weak gas monitoring capability of infrared imaging equipment is improved, so that the detection requirement of leakage of C4F7N / CO2 / O2 mixed gas is met, and the like.

The invention relates to an image recognition method for an electric power inspection scene, and the method comprises the steps: obtaining electric power inspection image data, carrying out the data enhancement processing, achieving the data amplification through a generative adversarial network, constructing an electric power inspection image data set through marking, and carrying out the sample balance processing; for a convolutional neural network model trained on a large-scale data set in advance, performing fine tuning by using an electric power inspection image data set; the method comprises the following steps: acquiring a to-be-identified electric power inspection scene image, inputting the image into a convolutional neural network model, extracting multi-scale features of the image in a residual connection mode, obtaining high-dimensional fusion features through cascade fusion or weighted fusion, improving the attention on a region of interest through an attention mechanism, and performing identification by using multiple models. And fusing the recognition results of the plurality of models to obtain a final image recognition result. Compared with the prior art, the method has the advantages of being good in performance, low in false detection rate and omission ratio and the like on power inspection image data with small volume.

The invention discloses a detection method, a detection system and equipment for monitoring a pipeline crack based on a 3D scanning image, and relates to the technical field of pipeline crack detection.The method comprises the steps that pipeline 3D scanning image data and pipeline design parameters are obtained, and the pipeline crack is detected according to the pipeline 3D scanning image data and the pipeline design parameters; judging a first detection result and crack characteristics of a potential crack area on the surface of the pipeline; generating a crack feature comparison table for comparing and adjusting a crack detection model according to the pipeline design parameters, the first detection result and the crack features; acquiring a crack detection reference range representing crack detection precision, and generating a crack detection model according to the crack detection reference range and the crack feature comparison table; acquiring pipeline high-resolution image data, inputting a pipeline high-resolution scanning image into the crack detection model to identify cracks on the surface of the pipeline, and generating a second detection result; the detection precision of pipeline crack detection is improved, and the false detection rate is reduced.