11results about How to "Suppress false detection" patented technology

The application discloses a kind of based on YOLO and gray scale template matching's firmware weld joint identification method, comprising the following steps: constructing image dataset, performing data enhancement, creating weld joint template, training YOLO benchmark detection network, parallelly executing YOLO detection and template matching, running adaptive fusion network, output final identification result.The beneficial effects of the present application are: introduce geometric distance similarity d_geo as forced constraint feature, when the space deviation of YOLO detection frame and template matching position exceeds the preset threshold, it is constrained to zero, and the false detection is inhibited from mechanism;A specific template intercepting ratio is used, so that the template retains the geometric structure features of the weld joint and avoids introducing interference information from the curved edge;Horizontal flip, brightness adjustment, Mosaic splicing are used as mandatory links in the training phase, so that limited samples can produce sufficient variation in training, and support the detection network to obtain effective generalization ability under limited data conditions.

In order to suppress false detection based on a resolver, it is necessary to shield electromagnetic noise generated from a rotor or a gap between the rotor and a stator. A motor unit has a motor and a resolver that detects a rotation angle of the motor. The motor has a stator and a rotor that is disposed at a position more inside than an inner peripheral surface of a stator core and is rotatably supported. The resolver has a resolver rotor that is mounted on a rotor shaft that is a shaft of the rotor and is rotatable with the rotor shaft, and a resolver stator. The motor unit further has a shield plate that is made of a magnetic body and is disposed between the rotor and the resolver stator, and an intermediate member that is interposed between the shield plate and the resolver stator, holds the shield plate at a position separated by a prescribed distance from the resolver stator, and is made of a non-magnetic body.

The invention discloses a remote sensing residential area extraction method and system based on multi-source data fusion semantic segmentation, and particularly relates to the field of remote sensing image processing and land utilization mapping. A multi-source fusion automatic labeling strategy is adopted, and a rural residential area pixel-level training label is automatically generated by using an impervious surface product, an open source map interest point and a night light learning area-brightness threshold value; the method comprises the following steps: constructing an SELPFormer lightweight Transform segmentation model, introducing Lite-PPM, SCSE and ELA feature enhancement modules and a lightweight decoder on the basis of SegFormer, and outputting a rural residential spot pixel level extraction result for regional scale rural residential spot mapping.

Provided is an abnormality diagnosis device for a fuel pressure sensor capable of suppressing erroneous detection of an abnormality of the fuel pressure sensor. A fuel pressure sensor diagnostic device for a vehicle that switches from one of a drivable state and a non-drivable state, in which the vehicle can be driven by at least one of an engine and a motor, to the other state in accordance with an operation by an occupant. A fuel pressure sensor diagnostic device for performing abnormality diagnosis of a fuel pressure sensor for detecting the pressure of fuel in a fuel supply path connected to a fuel injection device of an engine, the fuel pressure sensor diagnostic device being provided with: an estimation unit for estimating the temperature of the fuel in the fuel supply path when the engine is stopped; a diagnosis unit that, when the temperature is equal to or greater than a threshold value, performs abnormality diagnosis when a prescribed period has elapsed after the vehicle is switched from a travelable state to a non-travelable state as the engine is stopped; and a stop processing unit.

The invention provides an article identification method and device, a storage medium and a program product, and the method comprises the steps: obtaining a to-be-processed current article target, and enabling the current article target to be obtained through the target detection of a current frame image of a target region; target trajectory information of the current object target in continuous multi-frame images is determined, the continuous multi-frame images comprise the current frame image, and the target trajectory information comprises the corresponding interference suppression level of the current object target in the continuous multi-frame images; the interference suppression level is in positive correlation with the influence degree of an interferent on the current object target; calculating the proportion of image frames of which the interference suppression levels are greater than a preset level threshold value in the continuous multiple frames of images; and if the image frame proportion is smaller than a first preset proportion, carrying out loss prevention prompting on the current object target based on the interference suppression level. According to the invention, article false detection and false alarm caused by interferents are suppressed, and the article identification accuracy is improved.

This invention discloses a 3D target detection method based on sparse dynamic attention and star-shaped interaction. The method obtains basic voxel features from the original LiDAR point cloud through voxelization and sparse convolution, then introduces a sparse dynamic parallel attention module. This module achieves efficient enhancement of global context and channel dimensions through dynamic attention branches and parallel channel interaction branches. A sparse star-shaped interaction module is then used to construct a star-shaped neighborhood interaction structure with a central voxel, completing local geometric modeling and nonlinear feature interaction only on non-empty voxels. Finally, keypoint sampling, RoI pooling, and a detection head output the 3D detection box, category, and confidence score. This invention, through the synergistic complementarity of SDPA and SSB, significantly improves the detection accuracy of long-distance, small-scale, and occluded targets while maintaining linear growth in computational complexity and meeting real-time requirements. It achieves balanced performance optimization across multiple categories, including vehicles, pedestrians, and cyclists, and is suitable for 3D perception scenarios with high precision and real-time requirements, such as autonomous driving.

The objective of this invention is to suppress false detection of targets. A driving assistance device includes: a target processing unit that detects a moving object approaching from behind a vehicle and performs driving assistance control; the target processing unit performs the following processing: within the detection area of ​​a radar that detects the approaching moving object from behind the vehicle, a solid object determination area is set separately from the control object detection area of ​​the driving assistance control and at a predetermined distance from the radar; if the trajectory of the target object detected by the radar exists within the solid object determination area, the target object is determined to be a moving object with a solid body and the target object is used as the driving assistance control object; and if the trajectory of the target object detected by the radar does not exist within the solid object determination area, the target object is determined not to be a moving object with a solid body and the target object is not used as the driving assistance control object.

The application discloses a kind of lead seal thin wall structure's ultrasonic detection signal processing method, device and computer program product, method includes: obtaining the ultrasonic A scanning signal covering the region to be measured;Based on the signal characteristics of the defect-free area in A scanning signal, calculate adaptive detection threshold;To pre-process signal repeatedly executes iterative signal decomposition processing, until it satisfies the convergence condition based on the adaptive detection threshold.The iterative signal decomposition processing includes: based on the standard reference echo model of pre-set, at least two candidate echo components are positioned and constructed in current signal to be processed;Select a candidate echo component as effective echo component by competition mechanism;From current signal to be processed, the effective echo component is subtracted, and new signal to be processed is generated.The application is combined by statistical noise decoupling and competitive nonlinear iterative stripping, can effectively separate multiple defect echoes of time domain serious aliasing under strong noise background, improve the axial resolution of thin wall structure.

This invention provides a road maintenance discrimination method and system. The method includes: converting an initial road surface image into an enhanced illumination component and an enhanced reflection component to obtain an enhanced road surface image; obtaining a texture feature map corresponding to the enhanced road surface image, and then obtaining a global feature sequence; obtaining a determined disease category corresponding to the global feature sequence; obtaining a contour image based on the enhanced road surface image; obtaining a semantic feature tensor and a contour feature tensor through the enhanced road surface image and the contour image, and then obtaining a binarized segmentation map including the disease area; obtaining a road surface score based on the determined disease category and the binarized segmentation map to determine whether road maintenance is required. By obtaining the global feature sequence, the accuracy of disease category recognition is improved, and the combination with the enhanced road surface image enables accurate detection of early-stage diseases. The combination of the semantic feature tensor and the contour feature tensor effectively suppresses false detections caused by non-disease edges, further improving the detection accuracy of diseases.

A motor unit includes a motor and a resolver that detects a rotation angle of the motor. The motor includes a stator and a rotor that is disposed inward of an inner peripheral surface of a stator core and is rotatably supported. The resolver includes a resolver rotor that is attached to a shaft of the rotor, i.e., a rotor shaft, and is rotatable with the rotor shaft, and a resolver stator. The motor unit also includes a shield plate that is made of a magnetic material and is disposed between the rotor and the resolver stator in such a manner that the resolver stator is kept at a prescribed distance.

The present application relates to the technical field of computer vision, and particularly provides a single-target detection model training method, a single-target detection method, equipment and a medium, aiming at solving the problem of high false detection rate of the existing single-class target detector. To this end, the single-target detection model training method comprises the following steps: obtaining a first training data set, training an initial target detection model based on the first training data set to obtain initial weight parameters, wherein the prediction module of the initial target detection model comprises a position branch and a confidence branch, adding a classification branch to the prediction module of the trained initial target detection model to obtain a single-target detection model, and training the single-target detection model based on the first training data set and the initial weight parameters. Through the above implementation, the original model is improved, and the classification branch is added to determine whether the detected object is a "target object" or a "non-target object", which can effectively reduce the false detection rate of the model and improve the performance of the model.