215results about How to "Avoid false detection" patented technology

The invention relates to the technical field of engine cylinder surface defect detection, and discloses an engine cylinder surface defect detection method and system. The method comprises the following steps: acquiring three-dimensional height information of cylinder surface defect points based on a laser displacement sensor, identifying surface defects by combining an edge detection algorithm with a standard template library, calculating geometrical characteristics of the defects by fitting a polynomial and a least square method, and finally generating a defect report and feeding back the defect report to a central control system. Compared with a detection method in the prior art that fine defects on a complex curved surface are difficult to accurately identify, and especially in a data environment with large noise, accurate detection of micron-sized defects cannot be realized, the detection method has the advantages that noise is filtered through a high-precision laser displacement sensor and Fourier transform, so that the detection accuracy is improved, and the detection efficiency is improved. Precise detection and identification of surface fine defects are realized, so that the problems of missing detection and false detection in the prior art are avoided, and the detection precision and the production efficiency are improved.

According to the transformer substation construction safety distance monitoring method, device and equipment and the storage medium provided by the embodiment of the invention, the three-dimensional target detection model is established through the obtained point cloud data of the transformer substation, and the first three-dimensional target detection is carried out based on the three-dimensional target detection model so as to detect whether the electrified equipment exists in the to-be-monitored area; and under the condition of determining that the electrified equipment exists in the to-be-monitored area, obtaining three-dimensional position information corresponding to the electrified equipment, dividing a risk area based on the three-dimensional position information, and then performing secondary three-dimensional target detection based on the three-dimensional target detection model so as to detect whether an invader exists in the risk area. When the condition that an invader exists in the risk area is determined, the type of the invader is further judged, namely whether the invader is an invader or a construction machine is judged, and finally the Euclidean distance between the electrified equipment and the invader is calculated based on the boundary information; and if the Euclidean distance exceeds a preset distance threshold, outputting alarm information based on the category information.

The invention relates to a composition for identifying bladder cancer based on urine sample detection and application thereof, and belongs to the technical field of molecular biology. According to the composition disclosed by the invention, the bladder cancer is identified by detecting the expression of one or more of hsa-miR-423-5p, hsa-miR-155-5p and hsa-miR-590-5p in a urine sample; the composition comprises a specific reverse transcription stem-loop primer, a specific detection forward primer, a specific detection probe and a universal reverse primer. According to the composition disclosed by the invention, the expression of hsa-miR-423-5p, hsa-miR-155-5p and hsa-miR-590-5p in a urine sample is detected, and a binomial logistic regression model is combined, so that auxiliary judgment is performed on the bladder cancer, the sensitivity and the specificity are relatively high, and the accuracy of identifying the bladder cancer can be improved. The invention further provides a kit containing the composition, the detection object of the kit is a urine sample, and the kit has the advantages of being small in sample dosage, noninvasive in sampling, convenient, rapid, small in risk and the like.

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 provides a steel rail damage intelligent identification method and system based on YOLOv5, and belongs to the field of railway track flaw detection.The method comprises the steps that clutters in a steel rail damage B-display image are filtered, and the steel rail damage B-display image with the clutters removed is obtained; dividing the clutter-removed steel rail flaw B-display image into a flaw waveform and a non-flaw waveform, respectively performing data enhancement on the flaw waveform and the non-flaw waveform, and constructing a data set of a steel rail flaw identification model; training a steel rail damage identification model by using the data set to obtain an optimized steel rail damage identification model, inputting the clutter-removed steel rail damage B display image into the optimized steel rail damage identification model for identification, and outputting a steel rail damage identification result; the steel rail damage identification result is filtered, and the filtered steel rail damage identification result is displayed in a steel rail damage B display image; the steel rail damage detection precision under the complex scene of the steel rail track can be improved.

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 tank defect self-adaptive imaging method based on an ultrasonic flaw detection technology. The method comprises the following steps: S1, setting a driving path of an ultrasonic flaw detection trolley according to the detection requirement of a detected part; s2, the ultrasonic flaw detection trolley runs along a preset running path, an ultrasonic probe starts to work at the same time, ultrasonic waves are sent to the detected part, and a signal reflected back from the interior of the detected part is received and recorded and is transmitted back to the trolley data processing center; s3, the data processing center processes the ultrasonic signals by adopting a Golden Litsea rotundifolia algorithm based on a composite index CI to optimize a variational mode decomposition algorithm, then establishes a target function # imgabs2 # based on an algorithm approximation index value # imgabs0 # and a correlation coefficient index value # imgabs1 #, and performs denoising reconstruction on the signals by utilizing a self-adaptive reconstruction algorithm. According to the method, through signal processing and feature extraction, the gray level image of the internal defect condition of the anti-tank body is generated. Through analysis of the grayscale image, automatic identification and positioning of internal defects of the tank body can be realized.

The invention discloses a BSM-YOLO-based rat hole detection method, and belongs to the field of target detection, and the method comprises the following steps: introducing a BiFormer module and a Shuffle Attention mechanism into a YOLOv5 target detection model to construct a BSM-YOLO rat hole detection model; and inputting the acquired image data into the BSM-YOLO mouse hole detection model to obtain a mouse hole detection result. According to the invention, through the YOLOv5 deep learning algorithm, automatic detection of the mouse hole is realized, and dependence on manual detection is reduced.

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 discloses a ground resistance detection method and system for an electrostatic management tester, and relates to the field of ground resistance detection, and the key points of the technical scheme are that Op collection nodes are arranged in parallel on a circuit, voltage data on each node are obtained through a data collection device, and preliminary processing is carried out to obtain an initial data set; an optimization detection algorithm is used for the initial data set, and voltage data are converted into comprehensive direct-current voltage and comprehensive alternating-current voltage; setting an advanced detection threshold value, judging the obtained comprehensive direct current voltage and the comprehensive alternating current voltage with the advanced detection threshold value, performing mode selection according to a judgment result, giving an alarm if the mode is abnormal, and switching to a resistance detection mode and performing voltage measurement if the mode is normal. The device is suitable for detecting grounding resistance by an electrostatic management tester, and high-quality detection is realized.

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 utility model discloses a mutual inductor detection device, which relates to the mutual inductor detection technology field, and comprises a substrate, the substrate is provided with a conveying unit, a detection unit and a pickup unit, the detection unit comprises a first door frame, a first sliding driving cylinder, a second sliding driving cylinder, a first clamping driving cylinder, a second clamping driving cylinder and a third clamping driving cylinder, the first portal frame is provided with a first sliding driving cylinder, and the output end of the first sliding driving cylinder is provided with a first clamping driving cylinder; a second sliding driving cylinder is arranged below the first sliding driving cylinder, and a second clamping driving cylinder and a third clamping driving cylinder are arranged at the output end of the second sliding driving cylinder; a first electric clamping jaw is arranged at the output end of the first clamping driving cylinder; a second electric clamping jaw is arranged at the output end of the second clamping driving cylinder; a third electric clamping jaw is arranged at the output end of the third clamping driving cylinder; the copper cylinder pin detection device can replace manpower to clamp and detect two ends of a copper cylinder and pins at the same time, so that the detection efficiency is improved, and the quality is ensured.

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 invention provides a detection device and a detection method based on an ultra-low pressure gas leakage detection agent, and relates to the field of component detection, and the method comprises the steps: coating the ultra-low pressure gas leakage detection agent on a to-be-detected part of a component, and obtaining an image of the to-be-detected part in real time to generate a detection image set; acquiring bubble information corresponding to elements in the detection image set to obtain two-dimensional data of each bubble in the image to be detected at different moments; segmenting a single bubble according to the two-dimensional data of each bubble, and calculating the three-dimensional data of the single bubble to obtain the three-dimensional data of each bubble; according to the three-dimensional data of each bubble, determining leakage severity corresponding to elements in the detection image set; and evaluating the leakage severity of the to-be-detected part of the component according to the leakage severity corresponding to each element so as to obtain an air tightness detection result of the to-be-detected part of the component. According to the invention, missing detection and false detection caused by subjective reasons such as fatigue and attention distraction of people can be avoided.

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 invention discloses a 5G NR-based PUCCH format 0 signal detection method and system, and relates to the technical field of signal detection, and the method comprises the steps: issuing a timing advance command to a terminal through a scheduler to eliminate the system time delay in a PUCCH format 0 baseband receiving frequency domain signal, carrying out the discrete Fourier transform and decyclic shift, and carrying out the demodulation of the system time delay in the PUCCH format 0 baseband receiving frequency domain signal; the method comprises the following steps of: acquiring received signal energy of different code channel resources, combining the received signal energy of different symbols of different antennas, acquiring a combined gain signal of each code channel resource, determining a measurement signal-to-noise ratio, and finally inquiring a protocol table to carry out DTX detection and decoding according to the measurement signal-to-noise ratio and a receiving scene to obtain bearing information of the signal. The sending opportunity of the terminal is adjusted through the scheduler, the influence of system residual time delay is basically eliminated, the PUCCH format 0 signal receiving performance in an actual system is improved, the accuracy of measuring the signal-to-noise ratio is improved, and false detection and virtual detection are avoided.

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 invention provides a target detection method, a vehicle, a computing device and a computer readable storage medium, and the method comprises the steps: obtaining historical sensor data corresponding to each of a plurality of historical frames, and enabling the plurality of historical frames to be before a current frame in a time sequence; generating a reference detection mark corresponding to at least one target object in the current frame according to historical sensor data of each historical frame; and generating a target detection result in the current frame according to each reference detection mark. Through the method provided by the invention, the problem of missing detection or wrong detection caused by a shielding problem is avoided, the detection effect of executing the target detection method is enhanced, and the detection accuracy is improved.