95results about How to "Avoid false detection" patented technology

The invention relates to a power transmission line multi-defect detection method and system based on improved YOLOv8s, and the method comprises the steps: collecting insulator defects and bird nest images on a power transmission line, and obtaining an initial data set; adopting an SAM large model to preprocess the initial data set to obtain a target data set; dividing the target data set into a training set, a verification set and a test set; taking the YOLOv8s network model as a reference model to construct an improved YOLOv8s network model as a power transmission line multi-defect detection network model; inputting the training set into a model for model training; inputting a verification set into the trained power transmission line multi-defect detection network model for model evaluation; and inputting the test set into the model to obtain a power transmission line defect detection result, and evaluating the model. According to the improved YOLOv8s network model, the problems of missing detection and false detection of defects can be avoided, the detection precision of the model is improved, and the light weight of the model is considered at the same time.

The invention discloses a media file query and retrieval method and system based on an AI dialogue scene, the method combines a mixed mode of vector retrieval and keyword retrieval, realizes accurate retrieval of cross-modal data, and can process various media files including texts, pictures, videos, PPT and the like. According to the method, a double-round scoring mechanism of a large language model (LLM) is combined, firstly, the relevancy of file content is scored, then secondary scoring is carried out according to the matching degree of a file title and user query, and it is ensured that the file most relevant to query is called. In addition, the system can further utilize a user feedback mechanism to continuously optimize the retrieval precision, and the user experience is improved. Compared with the prior art, the method has the advantages that the accuracy and the efficiency of multi-modal data retrieval are remarkably improved, the limitation of a traditional AI dialogue system during multimedia file processing is solved, and the method is suitable for intelligent dialogue application scenes needing to quickly and accurately obtain various types of media files.

The invention relates to the technical field of surface defect detection, and discloses a gearbox gear defect detection method. The method comprises the steps of collecting a full-surface image of a gear, performing multi-layer feature extraction through image preprocessing in combination with a convolutional neural network, performing gridding segmentation and defect probability calculation through a Bayesian discrimination model, performing defect detection and classification on a defect-prone area and a defect-non-prone area by adopting a single-category model and a multi-category model respectively, and obtaining a defect-prone area and a defect-non-prone area. Generating a defect report; compared with the problems that in the prior art, manual detection is relied on, the speed is low, precision is insufficient, and particularly under the condition of a high-speed production line or uneven illumination, a traditional detection technology is difficult to effectively recognize small defects, real-time and high-precision gear surface defect detection is achieved by combining automatic detection based on deep learning, and the detection efficiency is improved. Therefore, the problems of missing detection and false detection in manual detection are avoided, the detection efficiency and accuracy are remarkably improved, and the method is suitable for a modern automatic production line.

The invention relates to the technical field of remote maintenance of a power distribution cabinet, in particular to a remote maintenance system of an intelligent power distribution cabinet, which constructs a component operation parameter matrix by collecting current, voltage, component temperature, starting time and internal and external environment data of the power distribution cabinet in real time, and combines environment parameters of the power distribution cabinet to maintain the power distribution cabinet. And carrying out deep learning training by using a residual neural network algorithm. The method not only can detect common fault modes, but also can identify abnormal conditions caused by component aging, so that the problems of false alarm and missing alarm in a traditional fault detection method are solved. Through the residual neural network, the system can automatically learn normal and abnormal operation modes from historical data, and the accuracy of fault detection is improved. And the exception type is further pushed to the operation and maintenance personnel and is visually displayed, so that the judgment time and the round-trip times of the operation and maintenance personnel on the maintenance component are saved, and the maintenance efficiency is improved.

The invention belongs to the technical field of oil pipe monitoring, and discloses a cruise ship oil pipe oil spill monitoring system and method based on machine learning, and the system comprises a data obtaining module which is used for obtaining cruise ship oil pipe state monitoring data; the data processing module is used for preprocessing the acquired cruise ship oil pipe state monitoring data to obtain an oil pipe state comprehensive feature data set; the oil spill risk prediction module is used for training according to the oil pipe state comprehensive feature data set to obtain an oil spill risk assessment model, and predicting an oil pipe oil spill risk rate based on the oil spill risk assessment model; judging whether the cruise ship oil pipe has an oil spilling risk or not according to the oil pipe oil spilling risk rate; if the cruise ship oil pipe has the oil spilling risk, the oil spilling early warning module carries out graded early warning on the oil spilling risk and triggers a corresponding alarm instruction; the oil spilling source tracking module is used for acquiring oil pipe space position data and positioning an oil spilling position in combination with oil pipe video monitoring data; and comprehensive monitoring and efficient management of the cruise ship oil pipe state are realized.

The invention discloses a target object image region segmentation method and device based on image recognition. The method comprises the following steps: acquiring an acquired pine forest aerial image; based on the collected image, obtaining a processing image mixed with at least two regions through cutting; training a first target recognition model by taking the processed image as a training sample; constructing an initial second target recognition model based on the trained first feature extraction module and an untrained second region detection module, and training by taking the collected image as a training sample to obtain a second target recognition model; and performing pine wood nematode disease region boundary segmentation based on the image region of the initial position of the pine wood nematode disease region identified by the second target identification model and the neighborhood region image to obtain the pine wood nematode disease region boundary. According to the invention, the identification accuracy of the pine wood nematode disease tree area is improved, and the phenomenon of misidentification or missed identification of the pine wood nematode disease tree area is reduced.

The invention discloses a rice seedling leaf age intelligent identification system based on a depth camera, and belongs to the technical field of agricultural intelligent equipment and machine vision. Comprising a monitoring point gridding path planning module, a mechanical arm cooperative positioning data acquisition module, an RGB-D data preprocessing module, a dynamic foreground seedling accurate extraction module, a leaf age recognition confidence evaluation module, a leaf age data visualization presentation module and an integrated control strategy feedback module. RGB color features and connected domain analysis are combined to optimize a mask, a front single seedling is accurately segmented, background interference is eliminated, the confidence coefficient is dynamically calibrated through a deep learning model and image quality features, texture definition and edge continuity features are combined to generate calibrated confidence coefficient, confidence coefficient evaluation is directly associated with an agricultural decision, and the accuracy of the agricultural decision is improved. The leaf age result is generated only based on reliable data, the personnel rechecking demand is reduced, and the decision-making efficiency is improved.

The invention discloses a video image type fire flame real-time detection method, which not only has a remarkable advantage in operation speed and can meet the requirements of various real-time monitoring systems, but also is excellent in detection precision and ensures efficient capture of real flames. Besides, the flame-retardant flame-retardant coating still can keep excellent performance in a complex environment, and particularly shows extremely strong anti-interference capability when facing a warm light source similar to the flame in color or a moving interferent similar to the flame in shape. The common false detection problem in a traditional detection system is effectively avoided, and the reliability and safety of flame monitoring are greatly improved. By means of the method, unnecessary manual intervention can be reduced, early warning can be conducted in time in the early stage of fire, and powerful technical support is provided for fire prevention and disaster reduction.

A power line insulation detection device disclosed by the present invention comprises a machine shell and an abutting wheel arranged on the machine shell and used for abutting against a cable, the machine shell is provided with a determination structure for identifying a damaged position of a cable insulation layer, and the determination structure comprises a bidirectional screw rod which rotates on the machine shell in a fixed-axis manner and freely rotates in the vertical direction. The two-way screw rod is provided with two groups of traversing seats which synchronously move towards each other or away from each other in the horizontal direction, each traversing seat is provided with a directional assembly for limiting the horizontal position of the traversing seat, and the extension plate is provided with a guide assembly for driving the abutting wheel to freely rotate in the horizontal direction after the abutting wheel abuts against the cable. According to the cable insulation layer damage detection device, the movement path of the abutting wheel can be ensured to be accurate, the detection range covers the laying direction of a cable, meanwhile, the damage position of a cable insulation layer can be accurately identified and marked, the accurate fault positioning reduces the time for maintenance personnel to find faults, and the maintenance efficiency is improved.

The invention relates to the technical field of data processing, in particular to an operation state monitoring method for marble-imitated aluminum veneer stamping equipment, which comprises the following steps: inputting the thickness of an aluminum plate and a real-time control parameter sequence into a classification model to obtain an abnormal probability; if the abnormal probability is greater than a preset value, calculating a gradient value of the abnormal probability at each moment to determine an adjustment moment; clustering the historical control parameter sequence to obtain candidate parameters, and replacing the control parameters at the adjustment moment with the candidate parameters with the maximum priority to update the real-time control parameter sequence until an optimal control parameter sequence is obtained; if the abnormal probability of the optimal control parameter sequence is larger than a preset value, the operation state is abnormal, and otherwise, the operation state is normal. According to the technical scheme, false detection of the operation state caused by inaccurate control parameters can be avoided, and the state monitoring result can be accurately obtained.

The embodiment of the invention provides a position detection method, a vehicle control method, equipment, a vehicle, a medium and a chip, and relates to the technical field of automatic driving. The position detection method comprises the steps that sensing data sets collected by a plurality of different types of sensors of a vehicle are obtained; for each perception data set, determining whether a matching detection result corresponding to the perception data set is a first target result or not according to the similarity of the perception data at different moments in the perception data set; wherein the first target result is used for representing that the similarity between the target position of the vehicle at the target moment and the historical position of the vehicle at the historical moment is greater than or equal to a first preset threshold value; and under the condition that the matching detection result corresponding to the first sensing data set in the plurality of sensing data sets is a first target result, if the matching detection result corresponding to the second sensing data set is a second target result, determining that the target position is the same as the historical position.

The utility model relates to an automatic workpiece carrying and stamping production line which comprises a control panel, a feeding machine used for automatically taking, placing and straightening workpieces, a stamping machine used for stamping the workpieces, a receiving belt conveying line used for conveying the workpieces and a material frame used for storing the workpieces. The swing arm manipulator is used for transferring a workpiece of the feeding machine to one stamping machine, transferring a workpiece of the stamping machine to another stamping machine or transferring a workpiece of the stamping machine to the receiving belt conveying line; the material frame is positioned at an outlet of the receiving belt conveying line; according to the production line, automatic carrying and automatic punching of the workpieces and automatic straightening of the positions of the workpieces are achieved, the requirement for manual operation is reduced, and the production efficiency is greatly improved. The automatic operation process can effectively save time and labor cost, and the overall efficiency of the production line is improved.

The application belongs to the technical field of optical lens detection, and particularly relates to an optical lens appearance defect detection device, which comprises a supporting assembly, a feeding device is installed on the supporting assembly, a first discharging slide plate is installed at the discharging end of the feeding device, a mounting seat is fixedly connected to the supporting assembly below the first discharging slide plate, supporting rollers are rotationally connected to both ends of the mounting seat, and the cooperation of the structures such as the belt ring, the first visual defect detection camera, the pushing cylinder, the guide plate and the driving roller is achieved, so that after the optical lens is accurately positioned in the positioning groove formed in the belt ring, the first visual defect detection camera arranged on both sides is used to detect the end of the optical lens, then the pushing cylinder is used to push the optical lens to the driving roller, the driving roller is rotated to drive the optical lens to slowly rotate, the second visual defect detection camera is used to detect the circumferential side of the optical lens, and the mechanical hand is not needed to be used for carrying and aligning, so that the detection efficiency is greatly improved.

The invention relates to a steel bar reverse bending test device, and belongs to the field of steel bar detection devices. The device comprises a rack and a workbench rotationally arranged on the rack. A supporting shaft is arranged on the rack; the supporting shaft penetrates through the workbench upwards and is coaxial with the workbench. A plurality of mounting holes are formed in the workbench; a bending block is inserted into any mounting hole; a working plate is arranged on the rack in the vertical direction in a sliding mode. A sliding rail is arranged on the working plate; scale marks are arranged in the length direction of the sliding rail. A first supporting piece is slidably arranged on the sliding rail. A first through hole is formed in the first supporting piece; a second supporting piece is arranged on the working plate; a second through hole is formed in the second supporting piece; the steel bars sequentially penetrate through the first through holes and the second through holes. A first image sensor is arranged on the rack; the method has the technical effects of avoiding manual calculation of the steel bar bending angle and improving the steel bar detection precision.

The invention discloses a jig for identifying a DCB substrate defective product and an identification method of the jig. A jig for identifying defective DCB substrates comprises a bottom plate, a first sliding plate capable of moving along a first straight line is mounted on the bottom plate, and a second sliding plate capable of moving along a second straight line is mounted on the second sliding plate; the center of the first sliding plate and the center of the second sliding plate are each provided with a hollowed-out structure. The second sliding plate is provided with a limiting groove used for containing a product, the product comprises a metal ring, a transparent supporting film is fixed to the metal ring, and a copper-clad ceramic substrate is installed on the transparent supporting film. The outer edge of the bottom plate is connected with an infrared light-emitting mechanism through an external bracket; an air cylinder is installed in the center of the bottom plate, a telescopic rod of the air cylinder is connected with a marking pen, the telescopic direction of the air cylinder is the vertical direction, and the light-emitting direction of the infrared light-emitting mechanism faces the pen point of the marking pen. The position of the defective product and the position of the marking pen are determined through the infrared light-emitting mechanism.

The present invention relates to the technical field of patch lead feeding, and specifically to a feeding device for patch lead production, comprising a feeder, a translator 1 is arranged on one side of the loader, a conveying component 1 is arranged on the translation track of the translator 1, a clamping mechanism is arranged inside the conveying component 1 and in a placement frame located on the conveyor belt, and a conveying component 3 is arranged on one side of the translator 1. The beneficial effects of the present invention are as follows: sheet frames that fail the inspection can be taken out by an extraction mechanism and cleaned by a cleaning wheel, thereby avoiding false detection caused by dust, and defective sheet frames can be taken out directly from the production line for collection in time, thereby avoiding waste of patch materials and reducing the defective rate of products. In addition, sheet frames of different sizes can be fixed by the clamping mechanism, which can reduce the number of equipment required to be debugged on the production line, speed up the debugging speed, and thus improve the production progress.

The application discloses a cable surface sheath image preprocessing method, device and equipment and a medium, and relates to the technical field of image processing. The method comprises the following steps: collecting a cable surface sheath image and dividing the image into multiple regions; calculating the average gray value and standard deviation of all pixels in each region, and determining a deviation range based on the 3σ criterion; determining pixels with a gray deviation not within the deviation range as abnormal pixels; determining whether there are other abnormal pixels in the eight-neighbor domain of each abnormal pixel, filtering the abnormal pixel if there are no other abnormal pixels, and constructing a connected domain composed of abnormal pixels if there are other abnormal pixels; filtering connected domains with an area less than a threshold value, and retaining connected domains with an area greater than or equal to the threshold value, to obtain a preprocessed image. Through local adaptive statistics, connected domain analysis and selective processing based on the area, the application overcomes uneven illumination and reflection interference caused by a cable curved surface, and helps to reduce the false positive rate and the missed detection rate of subsequent defect detection.

The utility model discloses a sealing surface precision detection oil pipe joint detection device, especially in oil pipe joint detection technical field, including support plate, be provided with the flow guide detection subassembly on the support plate, the flow guide detection subassembly includes the first flow guide box of setting in support plate one side, one side of first flow guide box is provided with the second flow guide box. The utility model discloses through pump machine and pressure sensor cooperation, can accurate simulation downhole high pressure environment, and pressure regulation range adapts different specifications oil pipe joint detection demand, solves the problem that the existing device can not restore actual working condition, leads to the detection result and actual use scene disjunction, avoids the problem of the leakage of the detection, the misjudgment caused by the inaccurate simulation of the working condition.

The invention belongs to the field of wireless communication technology, and in particular to a broadband signal detection method based on scale iteration and spectrum compensation. Aiming at the problems that the existing broadband signal detection algorithm is easily affected by noise, has poor detection effect under low signal-to-noise ratio, has high complexity, takes a long detection time, and requires a large amount of prior information, the following scheme is proposed, comprising the following steps: Step A: A received signal is sampled by AD to obtain a broadband sampling signal, and the broadband signal is divided into multiple signal segments of appropriate bandwidths through digital channelization; the purpose of the invention is to make the noise floor estimation more accurate and avoid false detection caused by uneven noise floor for broadband signals by utilizing the characteristics of morphological filtering bandwidth screening; utilize smoothing iteration to overcome the error caused by strong pulses and white noise in the signal to signal parameter estimation, improve the detection performance of frequency band aliasing signals by spectrum compensation, realize blind detection of broadband signals, and improve detection accuracy.

The application discloses a converter automatic slag splashing control method and system based on deep learning, relates to the technical field of converter steelmaking, and acquires a current converter opening image in a slag splashing process in real time, then inputs the current converter opening image into a trained deep learning model, judges whether the current slag splashing type is in a non-slagging state or a slagging state, returns to step S1 if the current slag splashing type is in the non-slagging state, and determines the current slag amount according to the current converter opening image if the current slag splashing type is in the slagging state, and finally controls the oxygen lance to perform corresponding processing actions according to the current slag amount, so that the problem of slag block misjudgment caused by light and smoke interference in the non-slagging state is avoided, and the accuracy of slag splashing control is improved.

The invention provides a radar point cloud-based foreign object intrusion detection method and system in a working area, and the method comprises the steps: setting a detection area range according to the working space of a mechanical connecting rod, and dynamically generating a direction bounding box (OBB) of the connecting rod through obtaining the posture state information and size data of the working connecting rod in real time; spatial screening is carried out on original radar point cloud data, only point cloud data which are located in a detection area and are not located in an OBB are reserved, and background point clouds outside the detection area and point clouds generated by a connecting rod are filtered out; and performing multi-dimensional anomaly detection on the filtered point cloud data, and comprehensively judging whether a continuously existing foreign object intrusion event exists or not. Through a linkage mechanism of point cloud filtering, background modeling and clustering verification, high-precision, low-false-alarm and real-time industrial intrusion detection is realized, and the problems of poor adaptability, high false alarm rate and the like of a traditional method in a complex environment are solved.

The invention provides a color feature extraction and correction method and system for drug sensitive plate MIC value interpretation and a computer readable storage medium. The method comprises the following steps: acquiring a to-be-processed drug sensitive plate image; performing visual preprocessing such as exposure, contrast and white balance on the image according to the configuration file; high-precision alignment correction of the image is realized by using ORB feature detection and a BFMatcher matching algorithm; generating a transparent mask with an Alpha channel based on predefined hole position contour data, and performing non-destructive target segmentation on the aligned image; and converting the target region to an HSV color space, extracting H-channel hue features, executing cyclic correction, eliminating the problem of numerical value jump of the red region, and obtaining stable color feature data. The method has the advantages of being high in image registration precision, accurate in segmentation, stable in color feature, high in robustness and the like, and automatic and standardized interpretation of the MIC value of the drug sensitive plate can be achieved under different shooting conditions.

The utility model relates to equipment for detecting whether a terminal is in place in a rubber shell. The equipment comprises a substrate, a jig and a detection mechanism, the base plate is horizontally arranged; the jig is arranged at the front part of the substrate and is used for positioning a rubber shell provided with a terminal; the detection mechanism is installed on the rear portion of the substrate in a front-back telescopic mode, and the detection mechanism stretches out forwards to eject out the terminals which are not installed in place in the rubber shell. The device has the beneficial effects that manual observation and detection are replaced, false detection or missing detection is avoided, the product quality is ensured, and the detection efficiency is improved.

The application discloses a kind of remote sensing image frame loss quick detection method, belong to remote sensing image quality evaluation and processing field.It includes: remote sensing image is carried out equal proportion downsampling, generates remote sensing image thumbnail, detects and extracts the dark pixel area of remote sensing image thumbnail;Determine whether the image exists image edge area frame loss condition;Mask image is generated, and mask image is carried out morphological operation;According to the mask image after morphological operation processing, extract image connected domain, obtain the effective area of image;Determine whether the image exists image middle area frame loss condition.The application can quickly and accurately detect whether remote sensing image exists frame loss problem, and does not need to additionally make frame loss image sample data set, simple and easy to operate, effectively avoids the false detection of frame loss image itself characteristics susceptible to special scene interference, realizes the automatic quality evaluation of remote sensing image.

The invention discloses a refrigerant leakage detection circuit, a control method and heat exchange equipment. The circuit comprises a sensor used for detecting the concentration of a refrigerant; the main control chip is used for receiving the refrigerant concentration and judging whether the main control chip is abnormal or not according to the refrigerant concentration and / or the duration when the refrigerant concentration is not received, and the main control chip further comprises a sensor reset pin used for controlling a sensor to reset; the sensor state feedback pin is used for feeding back whether preheating of the sensor is completed or not; the chip reset pin is used for controlling the reset of the main control chip; the first end of the controllable switch is connected with the sensor state feedback pin, the second end of the controllable switch is connected with the chip reset pin, the control end of the controllable switch is connected with the switch control pin of the main control chip, and the controllable switch is used for controlling whether the sensor state feedback pin and the chip reset pin are conducted or not. According to the invention, abnormal detection results caused by sensor faults and main control chip faults can be solved, false detection is avoided, and the detection accuracy is improved.

The invention relates to a target detector training method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a plurality of pre-constructed training batches; each training batch comprises a plurality of style image samples of at least one vehicle external target instance; through a pre-constructed student detector and a pre-trained teacher network, respectively extracting a first feature vector and a second feature vector of a target annotation box in each style image sample; the teacher network is used for extracting feature vectors with invariable styles; constructing an instance consistency loss function according to the first feature vector; constructing a distillation loss function according to the first feature vector and the second feature vector; and training the student detector based on the preset detection loss function, the instance consistency loss function and the distillation loss function to obtain the target detector for detecting the external target of the vehicle, thereby solving the problem of insufficient cross-domain generalization ability of the target detector, and improving the cross-domain generalization ability of the target detector.

This application discloses a display device, comprising a display panel and a driving module. The driving module is connected to the display panel and is configured to drive the display panel in a first driving mode when it detects that the grayscale to be displayed on the display panel is less than or equal to a first preset grayscale. In the first driving mode, when the driving module detects that the grayscale to be displayed is greater than a second preset grayscale, it switches the first driving mode to a second driving mode, where the second preset grayscale is greater than the first preset grayscale. This application can reduce false detections caused by grayscale fluctuations and avoid display problems such as screen flickering.

The utility model belongs to the technical field of detection equipment provides a kind of vice instrument panel detection tool, vice instrument panel is provided with peg, peg is made of metal material, vice instrument panel detection tool includes: pedestal;Positioning seat, it is set in the top of pedestal, positioning seat is provided with the detection through-hole compatible with for vice instrument panel, and positioning seat and pedestal between there is installation space;Compression assembly, it is set in pedestal, compression assembly has the compression operating mode and loosening operating mode that can be switched to each other;Wherein, when compression assembly is in compression operating mode, compression assembly compresses vice instrument panel;When compression assembly is in loosening operating mode, compression assembly loosens vice instrument panel;Detection component, it is set in pedestal, and detection component is used to detect the peg on target vice instrument panel.The vice instrument panel detection tool provided by the utility model is simple in structure, can automatically detect the peg on vice instrument panel, and is higher in efficiency.

The invention relates to the technical field of microlens detection, in particular to an automatic microlens detection method and system, and the method comprises the steps: obtaining a stripe image of a microlens under the illumination of a stripe structure; preprocessing the stripe image to obtain a preprocessed image with a block area as a foreground; determining a rotation angle according to the boundary of the reference block region, and rotating the corrected stripe image to align the stripe with the row direction; segmenting the rotating image, identifying a connected block, solving an external frame to generate a block mask and a complementary set interval mask thereof, and obtaining a block image and an interval image; performing segmentation on the block image, taking a median value according to rows to replace and generate a block reference image, performing differential binarization to obtain a block defect candidate, and performing independent binarization and merging on an abnormal region; overlapping the interval image and the block mask, performing closed operation to suppress line noise, and then performing binarization inversion to obtain an interval defect candidate; and superposing the two candidate output defect mark images. According to the invention, the defect can be automatically identified and marked and output by the microlens under the stripe background.

The invention belongs to the technical field of image detection, and particularly discloses a printing quality visual detection method for a reflective product, electronic equipment and a storage medium, and the method comprises the steps: S1, collecting product image data with a reflective region, obtaining an ideal segmentation effect image through processing, and making a data set; s2, training and testing a semantic segmentation network model k-net; s3, collecting an image of a defect-free product, preprocessing the image, inputting the image into the trained semantic segmentation network model k-net, and processing the image to make a template image set; s4, preprocessing a to-be-detected image, and inputting the preprocessed to-be-detected image into the trained semantic segmentation network model k-net to obtain a segmentation result; s5, carrying out rough matching on the segmentation result and a template, and intercepting a region of interest in the segmentation result; and S6, in the region of interest of the segmentation result, performing fine matching on each printing body, checking the printing quality of each printing body, and completing the printing quality detection of the product.