14results about How to "Solving recognition problems" patented technology

The invention belongs to the technical field of oil-gas field development, and discloses a low-salinity formation water identification method, which comprises the following steps of: obtaining data corresponding to a test oil well through drilling data, and comparing step by step according to the obtained data to judge whether water is discharged from a formation; the invention further discloses a low-salinity formation water recognition device and equipment and a storage medium. The method is suitable for identifying the low-salinity formation water, whether water flows out of the formation or not is judged in real time by draining the test oil well, an important basis is provided for identifying the low-salinity formation water, and the defects of a formation water judgment method are overcome.

In view of the problem that the model is difficult to accurately identify during the recycling of used mobile phones, the present invention proposes a method for identifying the model of used mobile phones based on a convolutional neural network. The present invention uses an edge detection algorithm to analyze the regional features in the mobile phone inspection photo, constructs a feature extraction convolutional network with shared weights, evaluates the similarity between the regional features of the used mobile phone image and the standard sample, and realizes the rapid identification of the mobile phone model. The present invention maintains good rapidity and accuracy in mobile phone model identification in different scenarios, which can improve the efficiency of used mobile phone recycling and the economic benefits of recycling enterprises.

The invention discloses a cross-receiver radio frequency fingerprint identification method based on feature separation under a small sample condition, relates to the technical field of communication radiation source individual identification, and aims to construct a separation model to perform feature extraction and feature separation on IQ data, separate transmitter features, and identify transmitter categories by using the transmitter features. The separation model comprises a radio frequency fingerprint feature extractor, a transmitter feature extractor, a receiver feature extractor, a transmitter classifier and a receiver classifier. IQ data of different transmitters are respectively collected through different receivers, data enhancement is performed on the collected IQ data, the IQ data after data enhancement is used as a sample, and a separation model is trained by using the sample. According to the invention, the feature separation network is used to decouple transmitter features and receiver features, and radio frequency fingerprint identification across receivers can be realized; and meanwhile, through a data enhancement mode, individual identification under a small sample condition is realized, and the robustness of the identification model under different channel conditions is enhanced.

The embodiment of the invention discloses a machine vision line patrol method and device based on spot detection, equipment and a medium. The method comprises the following steps: acquiring a machine vision track image, and setting a plurality of preset one-dimensional coordinate values in the machine vision track image; according to each preset one-dimensional coordinate value, carrying out image extraction on the machine vision track image to obtain machine vision track sub-images corresponding to each preset one-dimensional coordinate value; performing spot detection on each machine vision track sub-image to obtain key point coordinates of a plurality of spots; determining a real-time steering angle of a track in the machine vision track image according to the coordinates of the key points; and controlling the machine to move according to the track in the machine vision track image according to the real-time steering angle. According to the machine vision line patrol method based on spot detection, the trajectory identification calculation amount can be reduced, the identification real-time performance is improved, and the applicability is high.

The invention discloses an omnibearing low-false-alarm-rate real-time detection method and system based on active and passive combination, and belongs to the field of airborne alarm. The method comprises the following steps: acquiring an infrared image of an area where a target is located through a real-time detection device; performing differential filtering calculation on the infrared image to obtain a differential image; setting a threshold value, and carrying out binarization target segmentation on pixel points in the image one by one to obtain a binarization image; carrying out mark fusion on a target by adopting a run-length coding method according to the binary image; carrying out target position prediction and track maintenance by adopting a sequence image target association technology; and judging a threat target by using the difference characteristics. According to the invention, the environment and the target are detected in real time through the detection device, and the detected data are sent to the information processing device; and processing the acquired data through an information processing device so as to select a threat target from the infrared image.

The present invention provides a method and system for drum score recognition based on image segmentation and template matching in the field of music score recognition technology. The method comprises the following steps: step S10, obtaining a drum score file, converting the drum score file into a drum score picture and preprocessing it; step S20, identifying the score lines in the preprocessed drum score picture; step S30, selecting each note on the score line based on column cutting to obtain a plurality of note pictures; step S40, creating a plurality of note template pictures, matching the note pictures with the note template pictures through the OpenCV template matching algorithm to recognize the note pictures. The advantage of the present invention is that the drum score can be accurately recognized under the premise of ensuring the integrity of the drum score image information.

The multi-view collaborative SAR image ship intelligent identification and classification method of the present application identifies multi-view SAR ships through information fusion evidence reasoning. First, the feature area of ​​interest of the ship is extracted based on the image interception and digging of bicubic interpolation. After dimensionality reduction through multi-view ship analysis, it is input into the ship identification classifier to obtain soft judgment and confidence probability output to form a corresponding evaluation matrix. Then, the improved evidence reasoning-assisted judgment is used to fuse the classification judgments under different viewpoints to obtain the final ship identification judgment result. It overcomes the limitations of previous single-view recognition, has strong system stability, reliability and anti-interference ability, greatly improves the accuracy of ship recognition, greatly improves the efficiency of ship recognition and classification in vast waters, helps the management services of waterways, ports, waters, etc., provides supporting basis and predictive decision-making, and has huge application value and economic benefits.

The present invention discloses a method for identifying small obstacles in parking spaces based on visual depth estimation, comprising the following steps: starting a parking space search function; searching for available pre-selected parking spaces; outputting available pre-selected parking spaces after searching; entering radar obstacle detection and parking space space fusion judgment: preliminarily determining whether the parking space is available, and if not, entering a new parking space search judgment; if available, identifying small obstacles in the parking space; determining whether the parking space status is confirmed for the first time, and if so, starting automatic parking and starting a second parking space status confirmation process during parking, that is, performing radar obstacle detection and parking space space fusion judgment again; if not, starting automatic parking, and closing the parking space search function after parking is completed. The present invention can accurately identify the status of small obstacles in parking spaces, including opening ground locks, and make parking space availability judgments without changing the hardware equipment at the parking lot end, thereby greatly improving the safety of automatic parking.

The invention relates to a user identification method and device, relay equipment, a storage medium and a program product. The user identification method is used for a relay directly connected with a base station in a multi-hop access network, and the method comprises the following steps: receiving a remote UE report notification, the remote UE report notification comprising a first remote UE identifier; sending a remote UE report message, wherein the remote UE report message comprises a second remote UE identifier and remote UE IP information; the second remote UE identifier is the same as the first remote UE identifier, and the TCP / UDP port range included in the remote UE IP information is allocated to the remote UE by the relay directly connected with the base station. By adopting the method, the user identification problem in the multi-hop access network can be solved.

The present invention discloses a method for counting the number of words in a translation manuscript. The method comprises: obtaining a translation manuscript to be counted; dividing the translation manuscript to be counted into text, header and footer, picture, and graphic to obtain a sub-item result; counting the number of words in the text in the sub-item result; applying a de-duplication algorithm based on the addition of MD5 Hash values ​​to the header and footer in the sub-item result to count the number of words in the header and footer that are not repeated; using a picture translation recognition model to identify whether the picture in the sub-item result is translated, and using OCR recognition technology to perform text recognition on the translated picture, and counting the number of words in the recognition result; using a corresponding statistical strategy to count the number of words in the graphic in the sub-item result, summing all the statistical results to obtain the total number of words in the translation manuscript; and outputting the total number of words in the translation manuscript. By implementing the method of the present invention, the statistical problem of picture text in the file can be automatically processed, and the statistics of words in various complex graphics can be supported.

This application relates to a method, system, and medium for extracting radio frequency (RF) signal features from unmanned aerial vehicles (UAVs). The method includes: decoupling the acquired RF signal of a target UAV using a time-frequency transformation algorithm to obtain a time-frequency distribution feature map; extracting spectral texture features from the time-frequency distribution feature map at different receptive field scales using a trained deep neural network model to obtain texture feature vectors; generating position feature vectors containing temporal logic information using the same deep neural network model; and performing a feature space fusion mapping between the texture feature vectors and the position feature vectors to generate the RF fingerprint features of the target UAV. This application achieves texture extraction based on dilated convolution and position perception based on self-attention. This dual-stream parallel network architecture simultaneously captures multi-scale spectral texture and long-range frequency hopping logic in the time-frequency domain, constructing a highly robust semantic fingerprint of RF signals and effectively improving the detection and recognition rate of UAV frequency hopping flight control signals.

The present invention provides a method for identifying and locating insulator strings of transmission lines based on deep learning. By combining the Faster R-CNN network model and the YOLOv3 network model, the problems of insulator string identification and defect location can be effectively solved. At the same time, through the data enhancement method and quantitative analysis of the method, the problem of insufficient insulator string data set can be effectively solved. The insulator string identification and location task is divided into two stages. The first stage uses the Faster R-CNN network model to identify the insulator string, and the second stage uses the YOLOv3 network model to locate the defect. Comprehensively considering various factors in the task, an insulator string identification network based on the Faster R-CNN network model and an insulator string defect location network based on the YOLOv3 network model are designed. The scheme has the advantages of higher accuracy and higher recall rate at present.

The present application relates to a kind of complete parking space inference methods based on parking space features, comprising steps 1: obtaining the panoramic image around vehicle and inputting panoramic image into feature detector;Step 2: according to the number of proximal parking space mark points in parking space entrance line boundary box, entrance line is paired with proximal parking space mark points;Step 3: according to the distance between parking space entrance line type and paired proximal parking space mark points, determine the type of parking space;Step 4: generate two groups of distal parking space mark points respectively located on the opposite sides of vehicle;Step 5: calculate the intersection-over-union of two parking spaces and vehicle, determine the distal parking space mark point coordinates of actual parking space;Step 6: according to the proximal mark point and distal mark point of parking space, locate parking space, obtain complete parking space.The present application only needs to detect parking space entrance line and mark point, and complete parking space can be inferred through simple calculation.The method is high in accuracy and small in calculation amount.

The present invention discloses a method for assisting the blind to take a taxi based on computer vision technology, which obtains the current user's road conditions through the websocket protocol and WeChat applet; obtains the vehicle coordinates by computer vision technology to track the vehicle and calibrates a unique ID for each vehicle; obtains the license plate coordinates of a certain number of frames by computer vision technology to calculate the vehicle height; obtains the distance between the person and the vehicle and the driving speed of the vehicle by the vehicle tracking frame and the principle of similar triangles of a monocular camera; selects the vehicle tracking frame that needs to perform license plate detection and license plate recognition according to the driving speed of the vehicle; obtains the coordinates and license plate number of the license plate by OCR technology to find the target vehicle; and feeds back the vehicle information corresponding to the target vehicle ID to the user through the websocket protocol. The present invention solves the problems of insufficient real-time performance of the prior art, the obstruction of the license plate, the inaccurate recognition of the new energy license plate, and the lack of software for assisting the blind to take a taxi.