12results about How to "Solve the low detection accuracy" patented technology

The application discloses a multi-modal false news detection method and device, and relates to the field of multi-modal cognitive computing. The method is used for providing in-depth mining and analysis of news information, realizing semantic enhancement of multi-modal information, and mining and application of news sentiment features. According to the BERT model, the first image caption information is subjected to feature coding to obtain a first caption representation and a first empirical representation. A first text representation is subjected to a self-attention mechanism, a full connection layer and averaging to obtain a sentiment representation of comprehensive analysis of the first text representation by all experts and a sentiment tendency evaluation value of comprehensive analysis of the first text representation by all experts. According to the attention weights of a plurality of different sentiment news processors and the first news processing features, news aggregation features for true or false classification are obtained. According to the news aggregation features for true or false classification and a false news detector, a prediction label of the first news is obtained.

The invention belongs to the technical field of artificial intelligence, and relates to a water immersion identification method and device, computer equipment and a storage medium, the method comprises the following steps: receiving real-time water immersion data collected by water immersion data collection equipment, the real-time water immersion data comprising real-time sensor data and real-time image data; performing state estimation processing on the real-time sensor data according to a Kalman filtering algorithm to obtain a water immersion state estimation value; performing visual verification processing on the real-time image data according to an image recognition technology to obtain visual water immersion confidence; performing fuzzy reasoning processing according to a fuzzy rule, the water immersion state estimation value and the visual water immersion confidence to obtain a fuzzy reasoning result; performing defuzzification processing on the fuzzy reasoning result to obtain an accurate alarm confidence coefficient; and confirming a water immersion identification result according to the accurate alarm confidence coefficient. According to the invention, through fusion analysis and intelligent reasoning of multi-source information, accurate identification and reliable early warning of the water immersion state are realized.

This invention discloses a method and apparatus for inspecting wafers. The inspection method includes: acquiring grain registration marks contained in the wafer and the wafer to be inspected; determining a grain registration path based on the wafer to be inspected and the grain registration marks; determining a detection path based on the wafer to be inspected; acquiring a grain registration image of the grain row while the wafer moves along the grain registration path; acquiring a grain registration mark template image; determining the deviation between the actual position and the theoretical position of the grain registration mark in each wafer row based on the grain registration image and the grain registration mark template image; when the wafer moves along the detection path and the wafer to be inspected image of the grain row is acquired, determining the region of interest image of each wafer based on the deviation; and determining the defect detection result of the wafer based on the region of interest image. The accuracy of wafer inspection can be improved by using the above inspection method.

This utility model provides a contact wire wear detection device, relating to the field of wear detection technology. It includes a busbar with a cleaning mechanism at its bottom. The cleaning mechanism includes a detection housing, a cleaning box fixedly connected to the outer surface of the detection housing, a cleaning rod rotatably connected to the inner surface of the cleaning box via a bearing, a cleaning brush fixedly connected to the outer surface of the cleaning rod, and two limiting rods fixedly connected to the outer surface of the cleaning box. Sliding blocks are slidably connected to the outer surfaces of the two limiting rods. This utility model achieves automatic cleaning of the contact wire inside the busbar before detection. Through the coordinated action of the cleaning brush and scraper, it can efficiently wipe away stains on the wire surface and promptly clean the dirt attached to the cleaning brush, avoiding secondary contamination. This solves the problem of low detection accuracy caused by stains on the wire surface in existing detection devices, significantly improving the reliability of wear detection data.

Embodiments of the present application disclose a kind of odometer detection method, device, electronic equipment and storage medium, method includes: the odometer associated data of odometer and the measurement source associated data of measurement source are acquired;Wherein, the odometer associated data includes odometer data and odometer state;The measurement source associated data includes measurement source data and measurement source state;In the case where it is determined that the odometer state is odometer delay effective and the measurement source state is measurement source effective, the deviation data of the odometer data and the measurement source data is calculated;According to the deviation data, the detection state of the odometer is determined.The technical scheme of the present application embodiment can improve the accuracy of odometer state detection.

The application relates to the technical field of oil filter detection equipment, in particular to an oil filter internal pressure detection device, which comprises a shell, a end cover located at the top of the shell, an oil inlet and an oil outlet arranged on the end cover, a filter core arranged in the shell, a detection assembly arranged at the bottom of the inner wall of the shell and used for detecting the internal liquid pressure of the filter core, and a liquid buffer assembly arranged in the filter core and used for buffering and guiding the internal liquid of the filter core to the detection assembly. The liquid buffer assembly is arranged to buffer the internal liquid of the filter core, so that the internal liquid of the filter core does not directly rush to the detection assembly, the problem of low detection precision of the detection assembly caused by uneven distribution of the liquid pressure value entering the filter core is solved, and the pressure detection precision of the detection assembly is improved.

The utility model discloses a detection device is used in machine tool accessory processing relates to accessory processing detection tool technical field, including the main part, the main part upper end is equipped with the connecting block, the connecting block upper end is fixed with the spacing block for installing spacing, the lower extreme of main part is equipped with the threaded hole, and the threaded hole is connected with the connecting column in screw thread, and the bottom of connecting column is equipped with the platter, and the platter is equipped with the adjusting mechanism, the utility model discloses the cooperation of the rotating ring of adjusting mechanism and extension block, and it is convenient for flexible expansion platter detection distance, has improved the stability of device when detecting large accessory, and further can realize the function of reliable detection of large accessory, and finally solved the low problem of the detection precision of existing accessory detection device.

The application provides an electroencephalogram fatigue detection method, device and computer readable storage medium. The method comprises the following steps: obtaining original sample data of an electroencephalogram, performing division processing on the original sample data of the electroencephalogram to generate positive sample data and negative sample data; inputting the original sample data, the positive sample data and the negative sample data into a multi-scale time sequence feature coding model to extract corresponding multi-scale time sequence features, performing graph construction processing on the original sample data, the positive sample data and the negative sample data according to the corresponding multi-scale time sequence features to obtain corresponding sample graphs, inputting each sample graph into a graph attention network model, training the graph attention network model, and obtaining a fatigue detection model; obtaining electroencephalogram data of a person to be detected, performing detection processing on the electroencephalogram data by using the fatigue detection model, and outputting a fatigue detection result. The problem of low detection accuracy of electroencephalogram fatigue detection in the prior art is solved.

This application provides a testing fixture for a thermistor, relating to the technical field of electrical component testing equipment. The thermistor testing fixture includes a support platform, a conductive heating band, a clamping mechanism, a power supply circuit, a heating band temperature sensor, and a controller. The power supply circuit heats the conductive heating band and places it over the test position on the support platform. Simultaneously, the clamping mechanism presses the test piece placed at the test position against the conductive heating band. The conductive heating band rapidly heats up, transferring the temperature to the test piece for detection. The controller controls the heating power of the conductive heating band based on the temperature detected by the heating band temperature sensor, thereby enabling accurate testing of the test piece. The thermistor testing fixture provided in this application has a fast response speed, is more suitable for practical scenarios, and solves the problem of low detection accuracy in existing thermistor testing fixtures.

The invention discloses an anomaly detection method and device for lighting equipment, a storage medium and an electronic device.The method comprises the steps that the target brightness level of each piece of lighting equipment in a target object is determined according to the ambient light brightness in a target area, and the target area comprises the target object; under the condition that each lighting device is lightened in sequence based on the target brightness level, respectively acquiring a first video image corresponding to lightening each lighting device; and according to each first video image and the abnormal light spot template image, performing anomaly detection on each lighting device so as to determine whether stains exist in the target object. According to the method and the device, the problem that the detection accuracy of water stain detection is low due to the fact that a water stain detection method in the prior art depends on manual inspection or passive image analysis can be solved.

The invention discloses a street garbage detection method based on improved YOLOv8s. The street garbage detection method comprises the following steps: making a street garbage data set and preprocessing the data set; on the basis of a C2f module in the YOLOv8s model, a FocudLearAttention self-attention mechanism is fused to form a C2f-FLA module, and the feature fusion effect of the model and the expression ability of the model to global context information are improved; an AIFI module of RT-DETR is introduced into a check layer in YOLOv8s, the AIFI module enhances the feature representation capability through a multi-head self-attention mechanism and a feedforward network layer, global context information and detail features can be captured more effectively, and the detection precision of a small target is improved; a CIoU loss function used in YOLOv8s is improved into a Focal-EIoU loss function, so that the problem of inaccurate positioning of street garbage detection is solved. The improved YOLOv8s model algorithm enhances the expression capability of the model for global context information, so that shallow information and deep information in the image are fully fused, and the image information can be processed more effectively. According to the method, the problem of low detection precision in a street garbage detection scene with complex background and multiple small targets can be well solved, and a relatively high detection speed is kept at the same time.