59results about How to "Reduce false detection rate" patented technology

This invention discloses a system and method for labeling automotive abnormal noise data, relating to the field of automotive NVH detection technology. The method includes: receiving raw signal data of automotive abnormal noise; performing time-frequency transformation on the raw signal to generate a time-frequency diagram; identifying key frequency ranges characterizing the abnormal noise features; then performing directional filtering to enhance the abnormal noise signal; identifying candidate time ranges for abnormal noise through an automatic detection algorithm; and outputting the final abnormal noise time range labels and key frequency ranges in a structured format after verification and correction. The system includes a data receiving module, a signal processing module, a display module, an intelligent labeling module, an audio playback module, a labeling output module, and an interaction module, achieving triple-assisted labeling through auditory, visual, and intelligent pre-selection methods. This solves the problems of low efficiency, low accuracy, and poor consistency in traditional manual listening methods, providing high-quality labeled data for deep learning classification of automotive abnormal noises.

This invention belongs to the technical field of aviation transponder pulse overlap signal separation methods, specifically relating to a separation method applicable to Mode A / C aviation transponder pulse collision conditions. By analyzing the differential characteristics of the Mode A / C aviation transponder signal waveform in the time and energy domains, the pulse collision signal is effectively split. Based on the waveform definition characteristics of the Mode A / C aviation transponder signal, the pulse frame is accurately recovered from the separated signal, ultimately completing the separation process of the Mode A / C collision pulse signal. This invention requires only a single-channel receiver to achieve signal separation, reducing system implementation costs and engineering difficulty, effectively improving the system monitoring capability in densely overlapping electromagnetic environments, and providing an efficient and engineered solution for transponder pulse signal separation tasks in scenarios such as air traffic surveillance and low-altitude airspace management.

This invention provides a method and apparatus for vehicle scratch recognition based on a cascaded semantic segmentation and target detection, belonging to the field of computer vision technology. The method includes: acquiring a vehicle exterior image to be detected; performing semantic segmentation on the vehicle exterior image to obtain a corresponding vehicle foreground probability map; binarizing the vehicle foreground probability map to obtain a binary mask; performing background removal processing on the binary mask to obtain a background-removed image corresponding to the vehicle exterior image; and calling a scratch detection model to perform scratch recognition on the background-removed image to obtain the vehicle scratch recognition result. This invention addresses the technical problem of high false detection rate in existing vehicle surface scratch recognition technologies.

The utility model discloses a desktop formula wire joint piece scattered silk defect detection device, including the shell body that is located on the desktop, the bottom inner wall of shell body is installed with high definition imaging mechanism, and the front panel on shell body is installed with detection mechanism, the high definition imaging mechanism includes the inclined base fixed mounting on the bottom inner wall of shell body, and the top inclined surface of inclined base is provided with shooting assembly and adjusting assembly, the detection mechanism includes the plug board of inlay formula fixed mounting on the front panel of shell body, and the inboard of plug board is fixedly connected with glass plate through connecting column, and the side fixed mounting of glass plate near plug board has multiple prisms that are circularly distributed, the utility model discloses through high resolution's industrial camera and advanced image recognition algorithm, can accurately identify the tiny defect of wire joint piece, effectively reduces the misjudgment rate, improves product quality, and simple operation simultaneously, and the design structure of desktop formula is compact, and the floor space is small, and is convenient for installation and operation.

This invention provides a method for detecting rotating debris in coal flow based on physical fuzzy modeling and scene-customized attention. The method includes: acquiring a clear coal flow image and then labeling non-coal debris targets in the image; establishing a dataset; constructing a debris detection network; constructing a physics-driven motion fuzzy mapping model; using the motion fuzzy mapping model to dynamically blur the clear coal flow image, generating training samples simulating motion fuzziness phenomena in a conveyor belt; training and optimizing the debris detection network; inputting a real-time coal flow image into the optimized network to obtain candidate detection results for non-coal debris targets; post-processing the candidate detection results and outputting the final filtered detection results. This invention can improve the stability and accuracy of debris detection under complex conditions of high-speed coal flow, reduce the influence of motion fuzziness and background interference, and provide technical support for the safety monitoring and automatic sorting of coal mine conveying systems.

The application discloses an online detection system and method for a digital printing film switch, and relates to the technical field of machine vision detection. The method comprises the following steps: acquiring an initial image of a product to be detected; processing the initial image to obtain a second image; detecting the second image to obtain a size defect result, a position defect result, a color difference defect result, a bubble defect result and a crack defect result of the product to be detected; obtaining a total defect detection result of the product to be detected according to the size defect result, the position defect result, the color difference defect result, the bubble defect result and the crack defect result; and sorting the product to be detected according to the total defect detection result of the product to be detected. The method improves detection precision and production efficiency, and effectively reduces the missing detection rate and the false detection rate.

This application discloses a security protection method and related products. The method includes: identifying abnormal behavior in the data to be processed corresponding to a target vehicle based on a preset security rule base and a pre-trained anomaly recognition model to obtain a security recognition result; if the security recognition result indicates a first anomaly level, then performing local security protection on the target vehicle according to a local protection strategy; if the security recognition result indicates a second anomaly level, then obtaining the collaborative protection strategy sent by the cloud platform corresponding to the target vehicle, and performing collaborative security protection with other vehicles according to the collaborative protection strategy. This improves the security of the entire vehicle-to-everything (V2X) environment.

The present application belongs to the technical field of remote sensing image target detection, and particularly relates to a remote sensing image open vocabulary target detection method. The present application realizes open vocabulary, knowledge-enhanced and interpretable target detection of remote sensing images by constructing a knowledge graph that can be incrementally expanded online and deeply cooperating the knowledge graph with a visual language model. The present application can understand and respond to any new category input by a user in real time without retraining, greatly improving the application flexibility. The present application simultaneously applies the prior information of the knowledge graph to two key links. At the task level, the task instruction text fused with the prior information of the knowledge graph is encoded into a task condition semantic vector, and the parameters of the visual language model are adjusted according to the task condition semantic vector. At the concept level, the prior information of the knowledge graph is input into the visual language model to construct a stable semantic embedding vector for the new category. The two cooperate to effectively solve the semantic drift and adaptation problems of general models in the remote sensing field.

This application relates to the field of copper rod defect detection technology, and particularly to an online method and related apparatus for determining subcutaneous porosity in copper rod castings. The method includes: acquiring first detection parameters and a transmission speed; a first detection device performing a first frequency sweep operation on a continuously moving copper rod casting based on the first detection parameters to acquire first detection information in real time; determining second detection parameters of a second detection device in real time based on the first detection information and the transmission speed; the second detection device performing a second frequency sweep operation on the continuously moving copper rod casting based on the second detection parameters to acquire second detection information in real time; and determining porosity defect information based on the first detection information, the second detection information, and the transmission speed; wherein, porosity defect information refers to the location of subcutaneous pores in the copper rod casting. The online method for determining subcutaneous porosity in copper rod castings provided by this application can improve the detection accuracy of subcutaneous porosity defects in copper rod castings.

This invention provides a visual detection method and system for coating uniformity, belonging to the field of image processing. The method includes: obtaining the corresponding pixels in each modal image based on the positions of pixels in four matching pairs among all matching pairs of the brightfield image of the coating to be detected and the brightfield image corresponding to each modality; dividing the brightfield image of the coating to be detected into several regions based on the value of each pixel in the H channel of the HSV color space; and obtaining the uneven coating region based on the grayscale value of the corresponding pixel in the darkfield image and the relative thickness of the corresponding pixel in the narrowband spectral image within each region of the brightfield image of the coating to be detected. This invention aims to solve the problem of relatively low accuracy in detecting coating uniformity using a single modal image.

The application discloses a kind of modular configurable tooling detection methods, belong to tooling detection field, specifically include: module configuration step, automatic detection step, detection result processing step.Before detection, detection group leader configures detection hardware according to detection plan, software platform.When the unit to be detected is placed in the area to be detected, detection personnel start main control machine, and start automatic detection process according to the configuration parameters of detection scheme module, detection strategy module and detection alarm module after authentication.After detection, whether the detection data is entered into database according to the parameters configured by data analysis module is processed by main control software;Whether detection report is saved or printed;The application is adapted to the detection needs of the unit to be detected constantly upgrading iteration by the modularization and configurability of detection hardware and software platform, and the problem that error reproduction is difficult, error chain is deep and difficult to locate is solved by error tracing module.

This invention discloses a bidirectional guided fusion method for image information, a target detection method, and a system. The fusion method includes: inputting the image information to be processed into a YOLO model to obtain a shallow feature image from the Backbone network and a deep feature image from the Neck network; decoupling the feature frequency bands of the shallow feature image and the upsampled deep feature image to obtain a shallow high-frequency feature map and a deep low-frequency feature map, respectively; analyzing the semantic information represented by the deep low-frequency feature map to guide the shallow high-frequency feature map to enhance details, resulting in a detail-enhanced image; evaluating the weights of each feature channel using the shallow high-frequency feature map to guide the deep low-frequency feature map to calibrate semantics, resulting in a calibrated image; and fusing the detail-enhanced image with the calibrated image. This invention solves the problems of detail loss and semantic ambiguity in existing target detection networks, especially for small-scale targets.

The application discloses a kind of based on hypergraph enhanced YOLOv11 frame weed detection method and device, method includes: based on YOLOv11 baseline network, reconfigures local-global collaborative main network, obtains first optimization network reconfiguration neck network is hypergraph enhanced semantic neck, to establish high-order space topological feature fusion mechanism, obtains second optimization network;Second optimization network is used as the final enhanced detection model, and based on the enhanced detection model, weeds in field are detected;Using the constructed data set and multiple public data sets, the enhanced detection model is comprehensively experimentally verified;The enhanced detection model after verification is used for weed detection.The device includes: processor and memory.The present application solves the problem that the weed detection model in the prior art has low detection accuracy due to insufficient global context perception, weak geometric deformation representation capability and missing high-order topological relationship in complex farmland scenes.

The application discloses a highway pavement disease detection method, system and equipment, and relates to the technical field of highway pavement image processing. The technical scheme points are: obtaining a to-be-detected pavement image of a highway; inputting the to-be-detected pavement image into a pre-trained disease area segmentation model for segmentation and extraction to obtain a disease area image of the highway pavement; wherein, a first discriminator and a first generator are used to form a first generative adversarial network, the first generative adversarial network is trained, and when the number of training reaches a maximum number, a disease detection model is obtained; the disease area image is input into the pre-trained disease detection model for detection to obtain a disease detection result of the highway pavement; wherein, a YOLOv8 neural network is trained, and when a first loss function converges, the disease detection model is obtained. The problems that the prior art cannot capture detailed information and has false and missed detection of diseases when detecting pavement diseases are solved.

This disclosure provides embodiments of a method, apparatus, device, and medium for testing application systems based on multimodal retrieval. One specific implementation of the method includes: acquiring a detailed design document; performing image conversion processing on the detailed design pages to obtain detailed design page diagrams; performing feature extraction processing on each obtained detailed design page diagram to obtain detailed design feature information; determining a target historical page diagram set; determining each target historical test point set; generating each test point set; generating each test case set corresponding to each test point set; and executing system testing tasks on the application system. This implementation can reduce the waste of computational resources when testing application systems.

The application relates to the field of nail art equipment and computer vision technology and discloses a nail curing method and system based on deep learning and spatial positioning, which comprises the following steps: when a UV-LED module irradiates a nail, an RGB-UV dual-spectrum camera is used to synchronously collect an RGB image and a UV image of the nail; the 3D contour point cloud of the nail is determined by using the RGB image and the UV image, and the 3D contour point cloud is marked with the RGB image to obtain a to-be-cured vertex set belonging to a to-be-cured area; the vector included angle between the normal vector of each to-be-cured vertex in the to-be-cured vertex set and the light source vector is calculated; after the angle-intensity control of a UV light source array is performed by using a DMD chip, the nail is subjected to adjacent LED light supplementing treatment by using the UV light source array, so that the final nail curing result of the nail is determined. The application can solve the defects of the prior art, such as the poor pertinence of the ultraviolet radiation range, the lack of a real-time virtual try-on function and the incomplete curing of the curved surface edge.

This invention discloses a method, terminal device, and storage medium for noise identification and filtering in lidar, comprising: clustering line segments according to distance similarity in the scanning order for each scan line, marking suspected noise line segments in each scan line to obtain a set of suspected noise line segments; performing rapid clustering on each suspected noise line segment in the set to obtain multiple targets, and deleting point noise corresponding to targets containing fewer than a threshold line segments from the point cloud; calculating the linear representation of the suspected noise line segments and determining whether it is a centripetal line, and deleting the linear noise corresponding to the centripetal line from the point cloud. This invention is applied to the field of lidar, solving the problem of identifying and filtering point and linear noise in lidar, with low time consumption, compatibility with any multi-line mechanical rotating lidar, strong generalization ability, and the ability to retain real target point clouds with similar noise while filtering noise, resulting in extremely low false detection and false detection rates.

The application provides a curved panel defect detection method and system. The method comprises the following steps: acquiring multiple perspective images of a curved panel from multiple perspectives; preprocessing each perspective image, performing projection transformation based on the preprocessed perspective image, and reconstructing a panoramic development map of the curved panel; performing defect recognition and detection based on the panoramic development map; and outputting a defect recognition and detection result of the curved panel. The method can accurately locate and determine defects, and reduces the false detection rate and the missed detection rate.

This disclosure provides a method, apparatus, electronic device, and storage medium for audio and video call quality detection, relating to the field of call technology. The method includes: before a call, detecting multiple first indicators of a terminal to obtain a first risk score; collecting call data during the call; during the call, detecting the terminal and the call data based on the first risk score to obtain a second risk score; after the call ends, determining a quality inspection intensity score based on the first risk score and the second risk score; and performing quality inspection on the call data based on the quality inspection intensity score to obtain a quality inspection result.

The utility model relates to microswitch detection device technical field, especially for a microswitch detection device, including device main part, device main part one side fixed connection microswitch, front is equipped with controller, the bottom of device main part is equipped with detachable magnetic attraction subassembly, the device main part extends adjustable detection arm, the detection arm end is equipped with contact type detection head and laser ranging sensor utility model discloses through laser ranging sensor real -time monitoring press displacement, combines the contact signal of three -jaw type detection head, only when both satisfy the condition simultaneously judges as effective trigger, the misjudgment rate reduces, three claw floating structure ensures that even detection head and switch surface exist inclination, still can keep stable contact, avoid the misjudgment that single point stress uneven leads.

This utility model discloses a fuel filler cap, including a fuel filler cap body and a vent valve assembly disposed within the fuel filler cap body and capable of automatically opening and closing to establish an internal and external communication channel. The vent valve assembly includes a valve core, which is subjected to a pre-tightening force to close the internal and external communication channel. The valve core can be controlled to deform, and this deformation overcomes the pre-tightening force and opens the internal and external communication channel. The fuel filler cap of this utility model, with its automatically opening and closing vent valve assembly, can effectively prevent oil emulsification at the fuel filler cap, significantly reducing the probability of false engine diagnostics.

This invention provides a method for recognizing the operating status of solar thermal collectors based on a structure-aware segmentation network, belonging to the field of solar thermal equipment detection technology based on computer vision. First, it acquires equipment images and video data under different operating conditions through multi-source visual acquisition methods, and constructs a multidimensional dataset containing structural semantic labels, status labels, and temporal labels. Then, it builds a structure-aware semantic segmentation model based on an improved DeepLab network, and achieves high-precision segmentation of key structural regions of the thermal collector by introducing structural prior coding and structural constraint attention mechanisms. On this basis, it combines a temporal consistency discrimination module to model structural changes in continuous video frames, achieving dynamic recognition of states such as occlusion, offset, damage, and abnormal installation angles. Finally, it fuses the segmentation results and status discrimination results to output the equipment operating status and anomaly type, realizing automated detection and analysis.

This invention provides a machine vision-based method and apparatus for detecting fabric defects, belonging to the technical field of fabric defect detection methods. The method includes acquiring a grayscale image of the fabric surface and generating a hybrid filter kernel to filter the pixels of the image; constructing a two-stream feature extraction model to extract microscopic and texture feature maps; using channel concatenation to construct channel attention weight coefficients and simultaneously generating spatial attention weight coefficients for each pixel; fusing the channel attention weight coefficients and the spatial attention weight coefficients of the pixels at multiple scales to generate a fused feature map; using a convolutional network to construct a heatmap of the feature map; constructing an adaptive threshold to generate a binarized image of the defect. The two-stream feature extraction model design improves the characterization of multiple types of defects. By generating weight coefficients through channel concatenation, important feature channels are magnified, focusing on suspected defect areas. The use of the heatmap reflects the thermal statistical characteristics of local areas.

The application discloses an intelligent fire point recognition method fusing a physical optimization model and deep learning, and comprises the following steps: step 1, threshold dynamic processing is performed according to a physical mechanism model to obtain an optimized adaptive threshold physical mechanism model; step 2, a U-Net network model is constructed; step 3, a YOLOv5 model is introduced to detect highlighted targets; step 4, the optimized physical mechanism model and the U-Net network model are used to make a decision to recognize fire points, and remote sensing fire point preliminary screening results are obtained; and step 5, the highlighted targets recognized by the YOLOv5 model are used as error elimination on the remote sensing fire point preliminary screening results, so that the overall precision of the remote sensing fire point preliminary screening result recognition is improved; the problems of fire point false detection and missed detection caused by a fixed threshold are solved, various heat source errors in urban areas that seriously interfere with fire point detection for a long time are eliminated, and the overall precision is further improved; and the problems of poor universality caused by inherent system errors of traditional single thought algorithms are overcome.

This invention relates to the field of Building Information Modeling (BIM), and discloses a method and system for collision detection of complex electromechanical pipelines based on a feature-enhanced neural network. The method includes: processing the electromechanical pipeline model file using a multi-source BIM data fusion and geometric-semantic joint feature extraction method to obtain initial feature representations of pipeline components; performing multi-layer convolution on the initial feature representations using a dynamic heterogeneous graph neural network method to obtain enhanced feature representations of the pipeline components; performing two-stream interaction on multiple pipeline component pairs in a candidate collision pair set based on the enhanced feature representations to obtain a collision pair set; and continuously optimizing the pipeline avoidance path based on the collision pair set to obtain a collision-free BIM model. This invention achieves high-precision identification and intelligent avoidance of multiple types of collisions in complex electromechanical scenarios.

This invention discloses an intelligent monitoring system and method for steel wire rope safety, belonging to the field of non-destructive testing technology for steel wire ropes. The system includes a three-layer sensor array, a data acquisition and processing unit, a digital twin computing platform, and an intelligent diagnostic and early warning terminal. The three-layer sensor array consists of a pulsed eddy current-leakage magnetic flux composite sensor array, a fiber Bragg grating stress-vibration monitoring unit, and a machine vision surface defect detection module. The pulsed eddy current-leakage magnetic flux composite sensor array includes 12 sensing units evenly distributed along the circumference, employing a three-frequency time-division multiplexing method to achieve layered detection from the surface to the core, with penetration depths of 0.65 mm, 3.5 mm, and 8.5 mm, respectively. The fiber Bragg grating stress-vibration monitoring unit includes three optical fibers and 36 fiber Bragg gratings, reconstructing the three-dimensional stress state of the steel wire rope. The system fuses multi-source data using Dempster-Shafer evidence theory, achieving a false alarm rate of less than 2%, realizing real-time online monitoring and accurate life prediction of the steel wire rope.

The application discloses a low signal-to-noise ratio direct spread signal detection method based on noise cancellation, and belongs to the field of communication spectrum sensing. The application adopts a minimum mean square error-based adaptive noise cancellation method, can track and eliminate non-stationary noise interference in real time, automatically optimizes filter parameters in an unknown channel environment, and enables a signal detection system to adapt to different background noise conditions. By using a cyclic spectrum analysis method, the cyclic stationary characteristics of the direct spread signal are extracted, and robust signal detection is realized in a low signal-to-noise ratio environment. The existence of the signal is determined through a characteristic spectrum peak on a non-zero cycle frequency, and the carrier frequency and pseudo code rate are further estimated, so that more accurate signal identification and parameter extraction are realized, the influence of noise uncertainty on the detection performance is avoided, and the robustness and reliability of detection are improved. Welch smoothing processing and short-time Fourier transform are combined, the variance of spectrum estimation is reduced when the cyclic spectrum is calculated, and the robustness of detection is improved.