6811results about How to "Accurate detection" patented technology

The invention provides a robot alarm processing method and system based on a target detection algorithm and a cloud platform, and relates to the technical field of robot alarm processing, and the method comprises the steps: carrying out the detection of an abnormal target based on a convolutional neural network target detection model of the cloud platform in combination with a residual structure, a channel attention mechanism and an improved anchor-frame-free target detection algorithm, performing feature fusion through a feature fusion network guided by depth information to obtain accurate three-dimensional representation data, constructing a dynamic anomaly association graph by using an improved time sequence graph neural network, and generating a multi-level decision sequence in combination with an improved hierarchical reinforcement learning algorithm, and finally, collaborative task planning is carried out through a task decomposition network to obtain a task allocation scheme, and the robot carries out exception handling according to the optimal motion track, obstacle avoidance track data, real-time pose data, key operation sequence data and contact force feedback data.

The invention provides a product surface printing quality detection method based on industrial vision, which comprises the following steps: acquiring paper type and ink type information used by a printed matter, and extracting material spectral characteristic parameters of a paper and ink combination from a pre-established material spectral characteristic database, comprising scattering reflection characteristics of the paper surface and absorption reflection characteristics of ink; for the area with the quality defect, analyzing the spectral data of the area, extracting refined spectral characteristics, accurately classifying defect types through a deep learning model, and analyzing the influence of paper and ink material characteristics on a defect forming mechanism; according to the defect type and the position information, similar cases are retrieved from a pre-established defect knowledge base, a defect cause analysis result and an improvement suggestion are obtained, the material spectral characteristic parameters are matched with defect characteristics, and an evaluation result of the influence degree of the material characteristics on defect formation is obtained.

The invention relates to the technical field of software engineering, in particular to a source code detection method, which comprises the steps of source code preprocessing, code dependency analysis, semantic understanding and analysis, code quality evaluation, dynamic behavior simulation, intelligent vulnerability detection and the like to realize comprehensive detection of potential vulnerabilities and quality problems in source codes. A support vector machine and a K-means clustering algorithm are combined, known and unknown vulnerabilities can be effectively recognized, in addition, a continuous optimization feedback mechanism is introduced, a mixed vulnerability detection model can be continuously optimized according to repair operation and feedback information of a developer, the detection accuracy is improved, and the coverage range is expanded. The generated comprehensive detection report covers code structures, vulnerability positioning, quality problems and optimization suggestions, supports integration of various output formats and development tool chains, helps developers to efficiently repair problems, and improves code quality and project progress; according to the method, the comprehensiveness, the self-adaptability and the user experience of code detection are effectively improved.

The invention discloses a road disease detection system and method based on multi-source data fusion. The system comprises a data acquisition module, a data preprocessing module, a disease detection module, a three-dimensional modeling module, a data analysis module and a maintenance module. The data acquisition module is used for acquiring road surface data and sending the acquired data to the data preprocessing module; the road surface data comprises road data, road disease data, road vehicle data, non-motor vehicle data and pedestrian data, compared with the prior art, more comprehensive and more accurate detection of road diseases is realized by fusing multi-source data; by means of multi-dimensional data acquisition of radar data, video data, three-dimensional point cloud data, geographical location information and the like, the limitation of a single data source is overcome; the data preprocessing module can effectively remove noise and abnormal values, accurately classify and mark diseases, and fuse data collected by different sensors, so that the quality and integrity of the data are improved.

The invention relates to the technical field of software reliability guarantee, in particular to a code vulnerability detection method based on efficient parameter fine tuning of a code pre-training model, which comprises the following steps: preprocessing source codes of a vulnerability data set; s2, converting the preprocessed source code, and extracting a token sequence and a code attribute graph path corresponding to the source code; s3, constructing a code vulnerability detection model, wherein the code vulnerability detection model comprises a semantic feature extractor, a structural feature extractor, a feature fusion layer and a classifier; s4, jointly training a code vulnerability detection model by using the token sequence corresponding to the extracted source code and the code attribute graph path; and S5, through the optimized code vulnerability detection model, outputting a result that whether the source code to be detected has vulnerabilities or not. According to the method, the model is finely adjusted through efficient parameter fine adjustment, training resources are saved, good performance is obtained, and the code vulnerability detection efficiency and accuracy are improved by fusing and learning semantic and structural features of source codes at the same time.

The invention discloses an intrusion detection method and device based on a graph neural network and a medium, and relates to the field of computer networks, and the method comprises the steps: constructing a graph structure according to network flow data in a current time window; the graph structure comprises a node set, an edge set, a node feature matrix, an edge feature matrix and an adjacent matrix; the adjacent matrix is used for representing a communication state between the nodes; inputting the graph structure corresponding to the current time window into a network node traffic type identification model to obtain the traffic type of each node; the network node traffic type identification model is obtained by training a preset graph neural network by adopting a training sample set; in a preset graph neural network, for an input graph structure, a Bi-LSTM layer is adopted to extract time feature information, and a graph convolution layer and a graph attention mechanism are combined to extract space feature information in a time dimension, so that a traffic type of each node is obtained. According to the invention, more accurate network intrusion detection is realized.

The invention discloses an automatic ship berthing and leaving method based on video monitoring and image recognition, which comprises the following steps: S1, acquiring real-time video data, and carrying out image preprocessing; s2, extracting target feature information by using a target detection model based on an OfficientDet algorithm; s3, generating environment sensing information by using a multi-modal sensor fusion technology; s4, based on the environment perception information, constructing and updating a three-dimensional dynamic map of the surrounding environment of the ship in real time; s5, performing global path planning by adopting a fast extension random tree-star algorithm; s6, local dynamic obstacle avoidance is carried out in combination with a two-stage neural network algorithm; s7, based on the global path planning result and the local dynamic obstacle avoidance strategy, the adaptive control system accurately adjusts the speed, course and attitude of the ship according to the real-time environment change; and S8, performing big data analysis and historical data mining by using the cloud computing platform. According to the invention, image recognition and deep learning technologies are utilized to realize automatic berthing and unberthing of the ship, and the method has the advantages of high efficiency and accuracy.

The invention relates to the technical field of intelligent detection of building materials, in particular to a real-time detection method for grain composition characteristics of muck. The method comprises the following steps: S1, acquiring original point cloud data; s2, particle point cloud clustering; s3, determining the particle size of the sieved particles; s4, particle morphological feature extraction; s5, correcting the mass of the particles; and S6, grading feature output. The method has the characteristics of high precision, high efficiency, low cost, wide applicability and the like in particle grading characteristic detection, is particularly suitable for grading characteristic detection of residual mud particles, can also meet the real-time detection requirements of related particulate matters in other fields, such as mineral particle screening, building gravel detection, agricultural granular fertilizer grading and the like, and has a wide application prospect. Rapid and accurate detection of grain composition characteristics in different industries can be realized, and the requirements of various industrial production and engineering application are met.

The invention relates to the technical field of intelligent temperature control, and discloses an intelligent temperature control lead-free tin bar production method and device.The method comprises the steps that data of a plurality of sensors of a production line are collected and preprocessed, and a time sequence data set is obtained; carrying out attention-guided position-sensitive multiple interpolation to obtain a target data set; constructing and training based on the target data set to obtain an anomaly recognition model; according to normal production data screened by the anomaly recognition model, analysis is carried out based on an implicit subspace recognition regression neural network of orthogonal basis decomposition and reconstruction, and a production process parameter model is obtained; based on the production process parameter model, a multi-task feature fusion framework is used for parameter optimization, target production process parameters are obtained, the problem of data missing in the production process can be effectively solved, abnormal conditions can be accurately recognized, self-adaptive process parameter optimization can be conducted according to the characteristics of different production lines or batches, and the production process parameter optimization efficiency is improved. Therefore, the production quality and efficiency of the lead-free tin bar are improved.

The invention discloses a bad traffic monitoring method and system based on an LSTM and a clustering algorithm, belongs to the technical field of network security, and efficiently converges network traffic by using a BPF technology. The method comprises the following steps: accurately identifying fragmented traffic through an LSTM network, and capturing a complex dependency relationship by using a special memory unit structure of the LSTM network; performing session recombination by adopting an FCM clustering algorithm, restoring a complete network session, and quickly identifying malicious traffic through feature matching; and finally, timely treatment measures are taken for the confirmed malicious traffic. According to the method, rapid and accurate detection and identification of bad website traffic fragmentation behaviors are realized, and the technical problem that a traditional method is difficult to deal with is solved; through efficient caching and recombination mechanisms, the efficiency and accuracy of malicious traffic processing are improved; intelligent disposal measures are adopted, and it is ensured that malicious traffic is effectively restrained in time; the overall protection capability of network security is improved, and enterprise network resources and user data security are protected.

The invention belongs to the field of electric power and the technical field of artificial intelligence, and particularly relates to an electric power field operation safety detection method and system based on a hybrid expert model. According to the method, the visual language model and the YOLO target detection model are combined, and efficient and accurate electric power field operation safety detection is realized under the condition of few samples through a hybrid expert model architecture. In the training stage, through multi-model cooperative training, an expert model set for different scenes and targets is constructed. In the detection stage, a two-stage detection mechanism is adopted, YOLO is firstly used for rapid preliminary detection, then VLM is used for rechecking a low-confidence target, and finally detection results are integrated to ensure the accuracy of detection. The method aims to effectively cope with complex environments and potential safety hazards in electric power field operation, safety detection efficiency and accuracy are remarkably improved, and compared with a single model architecture, the method shows wider application scenes and higher detection quality in an electric power field operation scene.

The invention discloses a remote sensing target detection method based on a convolutional neural network, and the method comprises the steps: 1, constructing a full-scale enhanced alignment detection network model OSEADet comprising Backbone, Neck and Head, the Neck is an enhanced feature pyramid network EFPN, adding a P2 detection layer on the PAFPN network, enabling a feature map P2 to pass through SPDConv, outputting a feature map rich in small target information, fusing the feature map with a feature map P3, and obtaining a feature map P3; carrying out feature integration by using a CSP thought and a CSPF module; the Head comprises three dynamic task alignment sharing detection heads DSTA, the DSTA comprises a bounding box regression branch and a classification branch which are respectively used for predicting a target position and a target category, the tasks of the three DSTA are combined through group convolution, task interaction features are learned from a plurality of convolution layers, joint features are obtained, interaction among the tasks is increased, and the tasks are dynamically adjusted and aligned through a TaskDecomposition layer; 2, carrying out training and verification on the OSEADet; and 3, the trained OSEADet is used for predicting the position and category of the remote sensing target, so that the detection precision is high, and the remote sensing target can be detected in real time.

The invention discloses a multi-class defect detection method and device based on a variational auto-encoder and a denoising network, and the method comprises the steps: obtaining an industrial product surface defect image, carrying out the preprocessing, and constructing a training set and a verification set; the training process of the method is divided into two stages: in the first stage, a preprocessed training image is input into a variational auto-encoder for training, feature distribution of the image is learned, and features of a normal image are captured by reconstructing the image; in the second stage, a denoising network and a feature guiding module are used for conducting secondary reconstruction on image features in the feature space of the variational auto-encoder, and therefore the reconstruction capacity and the multi-class reconstruction capacity of the model are enhanced; in the reasoning stage, multi-scale features of an original image and a reconstructed image are extracted through a feature extractor, feature comparison is carried out, and an abnormal scoring graph is generated. According to the method, normal information is reserved while the reconstruction capability of the variational auto-encoder is enhanced, and defect missing detection caused by reconstruction failure is effectively reduced.

The invention relates to the technical field of intelligent spraying control, in particular to a vamp spraying control method and system for an intelligent shoemaking production line, and the method comprises the following steps: collecting spraying speed, spraying quantity, pressure and coating quality information, analyzing the influence of parameters on coating uniformity, coverage range and drying speed according to the current spraying effect, and generating a parameter influence data set. According to the method, the influence of each parameter on the coating quality is calculated, the priorities of the parameters in the control process are sequentially determined, and then historical records and current monitoring data are compared, so that abnormal fluctuation can be identified, potential abnormal conditions and positions of future key objects can be predicted according to the data, and fault modes can be identified. And the predictability and the reaction speed of the system are improved. And in combination with an image processing technology, the coating thickness of the vamp spraying area is further analyzed through image comparison, so that the thin coating area, the thick coating area and the spraying missing area can be more accurately detected, and the spraying process is more stable and efficient.

The invention belongs to the technical field of computer vision, provides a real-time semantic perception system running on equipment, performs real-time instance segmentation and semantic mapping on a working environment, and provides a perception method based on lightweight real-time instance segmentation and semantic mapping. In the first part, firstly, a color image containing color and texture information is used for object classification and bounding box regression; cutting the original depth image into a depth image with an accurate geometric edge, and inputting the depth image into the truncation network to generate a mask of the instance; a second part: (1) calculating the attitude and size of a target object in the view of the camera through principal component analysis; (2) obtaining the category of the object in the view obtained by segmentation; (3) creating a point cloud of the depth image projection of the target object in a map; and then cross-frame tracking and fusion are carried out by using data association to realize the purpose of sensing. The method is mainly applied to automatic sensing occasions.

The invention provides a data security traceability method and system based on artificial intelligence, relates to the field of data security, and is used for solving the problems of relatively poor data security traceability accuracy and response delay in the prior art. The method comprises the following steps: updating a user behavior attention model, and carrying out multi-dimensional aggregation analysis on all data flow paths comprising abnormal nodes and abnormal edges; determining the path with the highest comprehensive score value in all the data flow paths as a target abnormal path; performing data security traceability analysis on the target abnormal path to obtain a traceability analysis result; and determining a chain creation rule, taking each data circulation event on the target abnormal path as a block in combination with the chain creation rule, connecting the data circulation events in series to form a traceability chain, and starting a corresponding safety response strategy according to a traceability analysis result, the safety response strategy being obtained through self-adaptive generation according to the traceability chain. According to the technical scheme provided by the invention, the accuracy of data security traceability is improved.

The invention discloses a mobile phone screen glass defect detection method based on improved YOLOv8, and relates to the technical field of mobile phone screen glass cover plate quality detection. The method comprises the following steps: simulating an industrial production environment, collecting mobile phone screen glass images, and constructing a data set; improvement and optimization are carried out on the basis of the YOLOv8 target detection network model, a mobile phone screen glass defect detection classification model is constructed, and parameters are trained and optimized; constructing a semantic segmentation model of the YOLOv8 network, segmenting the defects of the mobile phone screen glass image to be identified, and realizing detection and identification of different defect category edges; and fitting the actual damage size of the mobile phone screen glass defect range. The mobile phone screen glass defect detection system is suitable for accurate detection of mobile phone screen glass defect characteristics, can measure the defect range, and provides intelligent and efficient services for the mobile phone screen glass quality inspection process.

The invention relates to the technical field of radio monitoring, in particular to a spectrum analysis device and method for a radio monitoring system and a storage medium, and the method comprises the steps: obtaining a corresponding environment noise level in a monitoring process, adjusting a sensitivity threshold value corresponding to signal detection based on the environment noise level, receiving a corresponding to-be-analyzed radio frequency signal, and preprocessing the received to-be-analyzed radio frequency signal based on an adaptive filter; calling a pre-trained spectrum analysis model, inputting the preprocessed to-be-analyzed radio frequency signal into the spectrum analysis model, and obtaining a corresponding spectrum classification result; and calling frequency information and time sequence data corresponding to a radio frequency signal to be analyzed, obtaining corresponding spectrum analysis data based on the spectrum classification result, the frequency information and the time sequence data, and displaying key parameters in the spectrum analysis data through a graphical interface. According to the invention, the effectiveness of signal detection and identification can be improved.

The invention relates to an anti-deception-interference joint detection method and system based on space-time frequency, and the method comprises the steps: obtaining a signal source direction of a received satellite signal, and dividing the signal source direction into channels according to angles based on a beam forming criterion; based on a preset capture rule, initiating capture search for the signal in each channel, and synchronously performing correlation peak detection and array antenna anti-interference detection on the signal to obtain angle information of the signal in each channel and satellite PRN information in the capture process; null processing is carried out on the signal source directions of two or more satellite signals appearing at the same angle; outputting a capture result based on the capture rule, and tracking the capture result conforming to the capture rule; doppler frequency shift consistency detection and signal power calculation are carried out on the signals subjected to rotation tracking, and deception signals are recognized and fed back to the antenna array for filtering processing. The application has the effects of expanding the application range of the anti-deception jamming detection method and improving the anti-jamming capability of navigation positioning.

The invention relates to the technical field of image data processing, in particular to a wood composite board furniture surface defect identification method and system. And wood board images are collected from the surface of the wood composite board furniture. Adaptive grid division is carried out on the wood board image according to different scales, texture direction information and high and low frequency texture components are extracted, and a texture feature map is obtained; and extracting structure edge information of the wood board image to obtain a structure information graph. Matching the texture feature map with the structure information map, and calculating local mutual information of the texture feature map and the structure information map in each region; the method comprises the following steps: acquiring resolution images of a wood board image under different scales, and calculating a mutual information matrix of the resolution images; distributing an abnormal score for each matrix element based on the mutual information matrix; and dividing grids according to the abnormal score, calculating a correlation value of adjacent grids, and obtaining a defect area based on the correlation value. And carrying out defect classification on the defect area. According to the invention, interference of natural textures can be reduced, and defect identification of small defects and tiny changes can be improved.

The invention provides a petrochemical pipeline corrosion defect detection device and method, and relates to the field of pipeline defect detection. The detection device comprises a mounting plate, and further comprises a battery box mounted on one side of the mounting plate, and the extension structure comprises a transmission assembly arranged on one side of the mounting plate, a rotating frame rotationally connected to the outer side of the transmission assembly and a push arm rotationally connected to the outer side of the transmission assembly and rotationally connected with the rotating frame; a connecting wire connected with the controller is mounted on one side of the electromagnetic ultrasonic thickness measuring probe; the servo motor drives the lead screw to rotate, the moving frame drives the rotating frame to turn over, meanwhile, the auxiliary wheels and the moving wheels are tightly attached to the interior of the pipeline, the size adjusting function is achieved, the device can easily adapt to pipelines with different inner diameters so as to deal with various complex working conditions, the universality and flexibility of the detection device are enhanced, and the detection efficiency is improved. The problem that the size of a pipeline corrosion detection device in the prior art is inconvenient to adjust is solved.

The invention provides a rice seed vigor rapid detection method based on near infrared spectrum, and belongs to the technical field of spectral analysis. The method comprises the following steps: performing different degrees of aging treatment on rice seeds in a high-temperature and high-humidity environment, taking unaged rice seeds as a control group, collecting spectral data of the rice seeds subjected to different degrees of aging treatment, and preprocessing the data; taking out the seeds of each treatment group, and measuring physiological indexes including germination rate, germination potential and the like; based on the preprocessed spectral data, a data enhancement method is adopted to fully embody internal characteristics and category differences of sample data; a Conv1D-GRU framework is applied, the model can capture spatial features and time sequence information in spectral data at the same time, a full-connection layer predicts extracted comprehensive features by means of vitality, a classification result is output through a softmax layer, a hybrid optimization population search algorithm is used for further optimizing the performance of the model, and the performance of the model is improved. Therefore, the activity of the rice seeds can be rapidly detected according to different aging degrees of the rice seeds.

The invention relates to the technical field of financial data processing, in particular to a data processing method and system based on financial information bills. The data processing method comprises the following steps: acquiring multi-modal financial information bill data; and performing bill modal decomposition on the multi-modal financial information bill data to generate financial information bill modal decomposition data. Bond information price income prediction needs a large amount of financial data, such as market interest rate, bond expiration date, face interest rate and the like, the data are similar to the data form of bill modal decomposition, and a more accurate model can be obtained through standardization and feature extraction of multi-modal data. Therefore, valuable input is provided for bond price and income prediction. According to the invention, through multi-modal data fusion, counterfeited feature comparison modeling, abnormal bill clustering analysis and dynamic risk assessment, the security and accuracy of financial bill identification are improved.

The invention relates to the technical field of instrument calibration, and particularly discloses a gas detection instrument prediction calibration system and method. The analysis module is used for determining a detection method of the zero drift phenomenon of the electrochemical gas sensor to be calibrated based on the use environment type; the calibration module is used for determining real-time calibration or periodic calibration of the electrochemical gas sensor in response to the occurrence of the zero drift phenomenon and the abnormal opening state of the electrochemical gas sensor; the evaluation module is used for determining the long-term zero point stability degree or the real-time signal fluctuation degree to evaluate the calibration precision according to the calibration method; the adjusting module is used for determining whether to update the environment compensation coefficient and adjust the calibration interval duration for the next calibration according to the evaluation result of the evaluation module; according to the invention, the accuracy of analyzing the use environment of the electrochemical gas sensor is improved, so that the accuracy of analyzing the zero drift phenomenon is improved, and accurate calibration is realized.

The invention discloses an underwater target detection method based on multi-scale feature cross fusion, and belongs to the technical field of underwater target detection. According to the underwater target detection network, a serial double encoder-decoder structure is adopted, a lightweight encoder structure is adopted to perform multi-scale feature extraction, and enhancement processing is performed on a small-scale feature map through an MSFCA module, so that the purpose of fully extracting target features in an underwater target image is achieved; the decoder is responsible for gradually recovering and enhancing the features of the small-scale multi-channel feature maps, and splicing the feature maps reserved according to the down-sampling operation with the feature maps of the corresponding levels in the decoding process so as to enhance the feature propagation and compensate the loss generated in the down-sampling process in the encoder stage; and finally, carrying out a coding and decoding process, carrying out multi-level feature splicing, sending the features of three different levels obtained in the last decoding process into a corresponding detection head structure, and carrying out fusion output to finally realize accurate detection of the underwater target.

The invention relates to the technical field of intelligent management, and discloses a digital twinning-based production line abnormal data processing method and system, and the method comprises the steps: collecting digital twinning model data of a production line, obtaining the number of equipment of the production line, and determining a data analysis period; acquiring operation data of each device in the data analysis period, arranging the operation data into a data matrix according to a time sequence, and judging whether the device is abnormal or not; when it is judged that the equipment is abnormal, abnormal features are extracted and compared with historical operation data, and the fault type of the equipment is determined; determining equipment risk values according to the fault types and the abnormal characteristics, and sorting abnormal equipment; and correspondingly traversing the historical operation data of each abnormal device, obtaining a historical influence value, judging whether the sequence is adjusted or not, and obtaining a final abnormal device sequence. The anomaly detection precision of the production line is improved, and the maintenance cost of the production line is reduced.

The invention discloses a pickling waste liquid recycling system and recycling method, and relates to the technical field of industrial wastewater treatment, the pickling waste liquid recycling system comprises: a treatment unit for respectively detecting the concentration of each heavy metal ion in pickling waste liquid, respectively adding corresponding chelating resin into the pickling waste liquid according to each heavy metal ion concentration, and collecting to obtain recycled acid liquid; the monitoring unit is used for collecting the pH value and the conductivity data of the recycled acid liquor in real time, inputting the pH value, the conductivity data and the heavy metal ion concentration data as monitoring data into the control unit, and establishing a monitoring database; and the control unit is used for calculating optimal control parameters in real time based on the monitoring data in the monitoring database, automatically adjusting the processing unit according to the optimal control parameters, and conveying the recycled acid liquor to the pickling process for recycling. Through multi-parameter cooperative monitoring and intelligent self-adaptive control, precise detection, dynamic optimization treatment and efficient recycling of the heavy metal ions in the pickling waste liquid are realized.

The invention discloses an intelligent detection system for the surface quality of a copper foil. The intelligent detection system comprises an acquisition module, a construction module and a detection module, the acquisition module is used for acquiring a multi-angle multi-spectral image of the surface of the copper foil and preprocessing the multi-angle multi-spectral image; the construction module is used for carrying out automatic identification and positioning on the surface of the copper foil by utilizing a defect detection algorithm based on deep learning based on the preprocessed multi-angle multispectral image, and constructing a copper foil surface defect identification model; and the detection module is used for comprehensively detecting the surface defects of the copper foil to be detected by using the copper foil surface defect identification model. According to the invention, comprehensive and accurate detection of surface defects of the copper foil can be realized, and the quality control level of the copper foil is improved.

The invention discloses a 3D target detection method based on millimeter wave radar and camera image double-branch mutual fusion. According to the method, specific image features of respective modes are efficiently extracted from a camera and a millimeter-wave radar, and complementary branch information is introduced. In particular, the method generates geometrically perceived camera bird's eye view (BEV) features and semantically perceived millimeter wave radar BEV features. The two types of features are fused to form a comprehensively enhanced BEV feature map, and then through decoding processing, accurate 3D target detection is realized. According to the method, the accuracy and robustness of 3D target detection are remarkably improved, and experimental results show that the method can effectively detect and position the 3D target in the environment even under the conditions of complex scenes and severe weather.

The invention discloses a pipeline defect recognition system based on a target detection algorithm, and the system comprises an image obtaining module which is used for obtaining image data in a pipeline, and a defect recognition module which is connected with the image obtaining module and is used for processing the image data based on the pre-trained target detection algorithm, recognizing defect features in the image data, and sending the defect features to the image obtaining module. The result generation module is connected with the defect identification module and used for generating a defect identification result, and the output module is connected with the result generation module and used for outputting the defect identification result to terminal equipment; according to the pipeline defect recognition system based on the target detection algorithm, the problems that in the prior art, image preprocessing becomes difficult, extraction of high-quality target features is affected, and then defect recognition precision is affected are solved, the problem that the detection effect is difficult to keep consistent is solved, and the problems that the environment challenges the real-time performance of the algorithm, the detection speed is insufficient, and the detection efficiency is low are solved. And the timeliness and practicability of defect detection are severely influenced.