14740 results about "Image manipulation" patented technology

A modeling method for three-dimensional real-time measurement reconstruction, comprising: on the basis of a three-dimensional camera, a laser radar, and an infrared sensor device, generating a multi-source original data set; performing data preprocessing; performing feature extraction and data fusion; performing three-dimensional space construction; by means of a deep machine learning algorithm, performing real-time dynamic adjustment and optimization on a model; and, by adopting an advanced image processing algorithm, generating a high-precision three-dimensional model.

The invention discloses a data center intelligent operation and maintenance method and system based on an industrial internet of things, belongs to the field of image processing calculation, and aims to solve the problems of privacy protection deficiency, serious manual dependence and weak energy efficiency and safety. Full-link dynamic optimization is realized through an edge calculation layer and an adaptive clustering network, edge nodes are integrated with a wavelet transform noise filtering technology, dynamic denoising is performed on multi-band sensor signals of temperature, vibration, current and the like, heterogeneous device time sequence data are aligned in combination with a dynamic time warping algorithm, sampling frequency differences of multiple devices are eliminated, and the multi-band sensor signals are obtained. A weight formula is put forward, a cluster head is dynamically elected based on node residual energy, data similarity and a load coefficient, a transmission path of a high-delay area is optimized in combination with a multi-hop routing protocol, the packet loss rate and the signal strength are evaluated in real time, redundant coding is started for a high-packet-loss link or the high-packet-loss link is switched to a low-power-consumption mode. And synchronizing the dynamic topology state with the digital twin platform through an open platform communication unified architecture protocol.

The invention discloses a glass lens surface scratch detection method and system, relates to the technical field of precision optical detection, and aims to solve the problems of scratch false detection, leak detection and poor algorithm adaptability caused by interference fringes, noise coupling and poor form adaptability in a high-reflection / complex coating process scene in the prior art. According to the scheme, an orthogonal polarization state composite light field is generated based on a multi-angle polarization light source array and a near-infrared compensation light source, and candidate regions are extracted through dynamic threshold segmentation and a direction gradient tensor matrix; gaussian pyramid multi-scale feature fusion and refraction angle consistency verification are utilized to eliminate artifact interference; constructing a direction constraint convolution kernel group to decompose scratches and background textures, and dynamically allocating computing resources in combination with a cascade network; feeding back closed-loop calibration light source wavelength and convolution kernel parameters in real time through coating parameters; according to the method, the precision and robustness of high-reflectivity surface scratch detection are remarkably improved, and meanwhile, the requirements for high-resolution image processing and real-time performance in a high-speed production line are balanced.

Embodiments of the invention provide a task processing method, a task platform, a computing device and a computer readable storage medium. The image processing method comprises the steps of obtaining a task description text of a target task; extracting task elements of the target task from the task description text by using a large language model, determining a plurality of target agents and target skills of the target agents from the candidate agent set based on the task elements, and generating a structured execution protocol corresponding to the target skills of the target agents; based on the structured execution protocols corresponding to the target skills of the multiple target agents, performing dependency analysis on the target skills of the multiple target agents to obtain a skill combination; and scheduling a skill tool corresponding to the skill combination to execute the target task to obtain a task result. The accuracy and the expandability of calling and processing the target task by skills and tools in an open scene are realized.

An example assistant system can use a multimodal multitask medical machine-learned model to perform image processing to answer natural language queries. A device can process speech data or other natural language inputs to obtain a query. The query can be processed alongside image data that provides context for the query. The example system can receive a query associated with a particular task domain; generate, based on the query, a query input that comprises query instruction data from a first modality and query context data from a second modality; generate a combined input comprising the query input and an exemplar input, wherein the exemplar input comprises exemplar instruction data from the first modality and an exemplar context placeholder in lieu of exemplar context data from the second modality; process the combined input with a multimodal machine-learned model to generate output data; and output a query response based on the output data.

A multi-modal articulation system translates human gestures into control commands for digital and automation devices. The system integrates a camera module, which captures facial and hand movements, with an image processing unit that extracts facial features, such as eyebrow movement, lip curvature, eyelid motion, and eye trajectory. These extracted features are analyzed in real time by an articulation recognition module that compares them against a database to recognize specific gestures. The system tracks and processes air-drawn hand gestures using a gesture trajectory tracking unit, which converts the motions into digital representations and classifies them using deep learning algorithms. The recognized gestures are converted into machine-readable control signals and executed on various connected devices via a relay control unit. The system incorporates adaptive algorithms to improve accuracy, account for environmental conditions, and stabilize hand tremors, providing a gesture-based control interface for applications ranging from smart home systems to multimedia devices.

The invention relates to the technical field of image processing, and discloses a video monitoring personnel behavior identification method and system. The method comprises the following steps: performing action node and joint angle extraction processing on a positioning area to obtain human body action skeleton sequence data; constructing an action connection graph and a time sequence association edge, and carrying out behavior pattern learning processing to obtain space-time behavior characteristics; performing action semantic mapping processing on the spatio-temporal behavior features and the human body behavior feature vectors, and performing behavior time sequence chain construction processing to obtain behavior semantic description features; and performing behavior pattern matching and anomaly measurement processing on the behavior semantic description features, performing feature discrimination processing to obtain a behavior type identifier and an anomaly degree index, performing behavior persistence analysis processing, and performing behavior risk level evaluation processing to obtain a behavior recognition result and early warning information. According to the invention, based on multi-modal feature fusion and spatio-temporal behavior modeling, accurate identification of personnel behaviors and dynamic assessment of risk levels are realized.

The invention relates to the technical field of image processing, and discloses a display method and system for a vehicle-mounted LED display screen. The method comprises the following steps: acquiring ambient light intensity data, performing fusion processing to obtain a characteristic value, and predicting ambient light change; identifying a driving scene according to the road condition data, and analyzing the priority by combining the ambient light change data; arranging a display area in combination with the eye movement data, and determining display parameters; analyzing the multi-modal interaction data, determining an interaction strategy, and verifying to generate response data; adjusting display content according to the ambient light features, and outputting a display effect in combination with response data; and energy consumption is estimated by integrating various data, and control parameters are generated to adjust an LED driving circuit. Through intelligent analysis and processing of multi-dimensional data such as ambient light intensity, driving scene and eye movement tracking, dynamic optimization and energy-saving control of display content are realized, and the problem that display effect and energy efficiency are difficult to consider in the prior art is overcome.

The invention relates to the technical field of image processing, and discloses a detection method, system and equipment for intelligent welding of pipe pile splicing and a medium. The method comprises the steps that noise reduction is conducted on a target weld seam image through a filtering algorithm, registration and splicing are conducted on the image through feature matching, and a weld seam unfolding image is obtained; segmenting the weld joint expanded image by using a deep learning network, and extracting a weld joint center line and surface features to obtain weld joint feature data; defect detection and recognition are conducted on the weld joint feature data, hierarchical calculation is conducted through a rule set, and weld joint quality data are obtained; parameter mapping is conducted according to the weld joint quality data, a welding parameter instruction is generated, welding process variables are adjusted, and target welding parameters are obtained; and the welding seam quality data and the target welding parameters are stored, and a quality report is generated through data analysis. The detection accuracy of intelligent welding for pipe pile splicing is improved.

The invention discloses an aluminum profile surface defect automatic detection method and system based on image recognition, and relates to the technical field of image processing and intelligent detection.The method comprises the steps that three-dimensional geometric data of the section of an aluminum profile are obtained through a three-dimensional scanning device, candidate observation angles and geometric feature parameters needed by coverage calculation are extracted, and the three-dimensional geometric data of the section of the aluminum profile are obtained; if the section shape contains a groove or a curved surface structure, marking a space coordinate range corresponding to the area of the shadow region to obtain a section geometric feature vector and a shadow region coordinate set; according to the aluminum profile surface defect automatic detection method and system based on image recognition, all-dimensional dead-corner-free detection of the surface of the aluminum profile is achieved, the method and system can adapt to the production takt of complex section shapes and changes, the accuracy and comprehensiveness of defect detection are improved, and effective technical support is provided for aluminum profile quality control.

The invention relates to an image processing technology, and discloses an efficient panoramic image splicing method and system based on multi-view fusion, and the method comprises the steps: collecting a plurality of images from different views; performing multi-scale feature extraction on each image, generating a feature descriptor for each extracted feature point, and matching a corresponding feature point pair; performing multi-view geometric constraint screening on the feature point pairs; according to the screened feature point pairs, estimating a homography matrix between adjacent images, and carrying out global optimization on the homography matrix; aligning all the images into the same coordinate system; determining an overlapping region between adjacent images; and according to the pixel information in the overlapping areas, fusing the overlapping areas by adopting a self-adaptive weighted fusion algorithm so as to splice the plurality of images into a panoramic image. The invention further discloses a control device and a computer readable storage medium. The invention aims to improve the efficiency and accuracy of generating the multi-view fused panoramic image.

The invention discloses a land degradation condition evaluation method and system, and relates to the technical field of image processing, and the method comprises the steps: collecting a land image through multi-source remote sensing, and classifying land organisms through multi-level extraction of remote sensing spatial features; constructing a space-time coupled dynamic particle evolution model based on the land organism distribution map, and performing dynamic particle evolution prediction on land organisms; establishing a non-biological value quantitative model, performing weight distribution according to historical data and a machine learning algorithm, and dynamically calculating a multi-dimensional value index system; and evaluating the land degradation condition according to the land abiotic value historical value and the dynamic particle evolution prediction of the land organisms. According to the method, adaptive feature extraction is carried out through the combination of deformable convolution and transfer learning, the classification precision under a complex terrain is improved, land degradation is evaluated by constructing a particle evolution model to quantify the space-time response of a biocenosis and constructing a multi-dimensional non-biological value index system at the same time, and the credibility of a block chain is enhanced.

The invention provides a remote sensing image super-resolution method based on a diffusion model and a multi-modal large language model, and relates to the technical field of image processing. According to the method, a pre-trained stable diffusion model is adopted, the model is trained on a large-scale data set, the stable and detailed super-resolution effect can be achieved, and the convergence speed is higher. According to the method, a multi-mode large language model is used for enhancing the semantic comprehension ability of the model, and detailed content description and texture description are provided. The invention also introduces an additional classifier which provides global information and a simple signal about the LR. The prior information is adaptively integrated into a diffusion model through a cross attention module. And finally, through combination of the strong generation capability of the stable diffusion model and the semantic understanding capability of the MLLM, the method provided by the invention is excellent in the aspect of remote sensing image reconstruction, and excellent performance is realized in the aspect of quality of a plurality of images on different data sets.

The invention provides an AI-based automatic identity card image cutting and OCR recognition method and system, and relates to the technical field of image processing, and the method comprises the steps: collecting a multi-angle depth image and a visible light image, reconstructing a three-dimensional model of a certificate through a three-dimensional point cloud registration network, and achieving the perspective correction of an image through the combination of attention posture estimation and projection matrix optimization; a double-flow memory neural network is adopted to carry out information area positioning, a feature extraction backbone network constructs a multi-scale feature pyramid, and a dynamic memory updating network carries out feature matching and dictionary updating based on a time sequence attention module and comparative learning; and performing character recognition on the segmented information sub-images through a multi-branch feature enhancement network, improving the recognition precision in combination with a self-attention enhancement unit and a context reasoning unit, and dynamically optimizing network parameters by using a reinforcement learning controller until a preset recognition credibility threshold value is reached.

The invention relates to the technical field of image processing, and discloses an AR technology-based precise positioning method and system for minimally invasive surgery of hepatobiliary surgery. The method comprises the steps that blood vessel network data are extracted through a medical image sequence, a three-dimensional structure diagram is generated, operation target points are marked, and a coordinate system is established; collecting dynamic data for mapping matching, and establishing a real-time tracking model; constructing an augmented reality scene and implanting a navigation path; acquiring an instrument position calculation track, and comparing with a navigation map to generate an instruction; constructing a projection interface to display the key area; recording the whole process and evaluating the operation quality. By establishing the real-time tissue deformation tracking model, a multi-source data fusion accurate positioning mechanism and an intelligent operation quality evaluation system, the accuracy and reliability of operation navigation are effectively improved.

The invention relates to a multi-source remote sensing image classification method based on a key band retrieval attention mechanism, and belongs to the technical field of remote sensing image processing. The method comprises the steps of firstly performing preprocessing and data set division on multi-source remote sensing data, then constructing a key band retrieval attention network, performing training and evaluation on a model by utilizing a training set and a verification set after division, and finally performing visual analysis on a model result by utilizing a test set. According to the method, the features of the hyperspectral image and the laser radar / synthetic aperture radar image are effectively extracted and fused, redundant information interference is effectively reduced, the retention rate of hyperspectral key information is improved, the complementary expression ability among multi-source data is enhanced, and the remote sensing image classification precision and calculation efficiency are remarkably improved. The method is suitable for application scenes of multi-source remote sensing data fusion and classification, can meet efficient intelligent processing requirements of complex earth surface information, and provides an accurate and efficient remote sensing image classification solution.

The invention discloses a CAD drawing segmentation parameter adjustment method and system combined with region recognition, and relates to the technical field of image processing, and the method comprises the steps: obtaining a target CAD drawing file, carrying out the region feature extraction, generating a region feature vector set, carrying out the region type recognition, and determining a plurality of pieces of region information; constructing a segmentation rule base, matching the segmentation rule base according to the information of the plurality of regions, and dynamically adjusting the boundary information according to a matching result to obtain a segmentation parameter set; based on the segmentation parameter set, performing adaptive segmentation processing on the target CAD drawing file to generate a structured drawing segmentation result; and based on the structured drawing segmentation result, carrying out topological relation verification, and generating a segmentation optimization result. According to the method, the technical problem of poor segmentation effect caused by inaccurate region identification and fixed segmentation parameters in the CAD drawing segmentation process in the prior art is solved, and the technical effects of improving the accuracy and adaptability of CAD drawing segmentation and optimizing the segmentation quality and the structural effect are achieved.

The invention relates to an image processing and data processing technology, in particular to an intelligent coagulant dosing control method and system based on image recognition and multi-parameter modeling, the floc state is accurately quantified through image recognition and deep learning modeling, and a dosing prediction model with self-adaptive capacity is established in combination with raw water feed-forward information. And accurate control of coagulant addition is realized. The method comprises the following steps: collecting a floc image, carrying out image processing and floc feature extraction, and constructing a floc image description vector; time sequence input structure data fusing the floc image and the water quality data is constructed, and floc image sampling at each moment is defined as a time frame; performing enhancement and reconstruction processing on the training data of the dosing amount prediction model by adopting a data enhancement and sample equalization strategy to obtain continuously distributed synthetic samples; and constructing a hierarchical feature fusion enhanced dosing amount prediction model, fusing the previous water quality parameters, the current water quality parameters and the floc image joint feature vectors, and optimizing the dosing amount prediction precision layer by layer.

The invention relates to a multi-source remote sensing image classification method based on spectrum adaptive feature fusion, and belongs to the field of remote sensing image processing and analysis. Aiming at the problems of poor feature extraction flexibility, poor feature fusion strategy, low calculation efficiency and the like of the existing multi-source remote sensing image classification method, the method comprises the following steps: firstly, preprocessing and dividing a multi-source remote sensing data set, then constructing a spectrum adaptive feature fusion neural network model, and then carrying out model training and quantitative evaluation; and finally, carrying out model visualization result analysis. The features of a hyperspectral image and a laser radar / synthetic aperture radar image are effectively extracted and fused, classification of multi-source remote sensing data is achieved, the method is suitable for a multi-source data fusion and classification scene, the remote sensing data processing requirement of complex earth surface information can be met, the advantages in the aspect of improving classification precision and processing efficiency are remarkable, and the method is suitable for popularization and application. And a more efficient and accurate solution is provided for multi-source remote sensing image classification.

The invention relates to the technical field of medical image processing, in particular to a medical image automatic analysis system and method, and the system comprises a time sequence dynamic monitoring module, a lesion area weighted fusion module, a boundary energy optimization module, a multi-channel filtering processing module, and a time sequence feature construction module. According to the method, time sequence tracking analysis of medical images is realized through time sequence dynamic monitoring, change salient region screening and precise lesion evolution trend positioning, lesion data at different moments are integrated by means of weighted fusion processing, the accuracy of time sequence analysis is improved, data fluctuation errors are reduced, lesion boundaries are optimized in combination with gradient analysis and gray scale comparison, and the accuracy of medical image analysis is improved. Noise interference is eliminated, lesion contours are clearer, different frequency features of images are screened through multichannel filtering, lesion differentiation information is highlighted, irrelevant interference is reduced, feature expression is enhanced, a complete lesion time sequence analysis framework is constructed, and a comprehensive image analysis basis is provided for precise medical treatment.

The invention discloses a real-time single-stage remote sensing image correction target detection method based on YOLOV8, and relates to the technical field of remote sensing image processing. According to the method, a deformable convolution dynamic prediction local geometric distortion parameter is embedded based on a YOLOv8 backbone network, an adaptive deformation field is generated, pixel-level real-time correction is realized, shallow details and high-level semantic features are fused through a bidirectional path aggregation network, and channel attention and a space gating mechanism are combined, so that the real-time correction of the image is realized. The small target detection capability is enhanced, background noise is suppressed, angle prediction is divided into discrete classification and continuous residual error regression tasks through a decoupling type rotation detection head, angle periodic errors are eliminated in combination with a direction sensitive loss function, and the rotation frame positioning precision is improved. And constructing a dynamic multi-task collaborative loss function, introducing gradient distribution consistency constraint to jointly optimize correction and detection tasks, and realizing feature semantic alignment and model self-enhancement through end-to-end closed-loop training. And the rotating target detection precision and the complex scene robustness are obviously improved.

The invention provides a multi-image forgery detection method and system based on a cross-modal vision large language model, and relates to the technical field of image processing and computer vision, and the method comprises the steps: constructing a data set, and carrying out the preprocessing of the data set; according to the preprocessed data, respectively extracting visual features and language features through a pre-trained visual Transform and a language model so as to obtain cross-modal features; according to the cross-modal features, the visual features and the language features are clustered, cross-modal similarity is calculated, a matching relation is established, fusion is carried out, and fused multi-modal features are obtained; and according to the fused multi-modal features, performing adversarial training through a generator and a discriminator to generate an adversarial network. According to the method, high-precision detection and effective detection of multiple image counterfeiting types such as positioning splicing, copying and pasting, AIGC generation and the like are realized.

The invention provides a microcrack precise segmentation method integrating feature fusion and convolution attention, and belongs to the field of image processing. According to the method, a crack segmentation network based on an encoder-decoder architecture is constructed, a convolution block attention module is introduced at an encoder end, background noise is adaptively suppressed and obvious characteristics of cracks are enhanced through a channel and space dual attention mechanism, and the method is suitable for the adaptive segmentation of the cracks on the premise of almost not increasing the calculation overhead. The sensitivity of the model to microcracks is improved; a feature fusion module is introduced at a decoder end, and cooperation of low-layer details and high-layer semantics is realized through cross-layer fusion, so that a semantic gap is effectively bridged, detail loss caused by traditional convolution stacking is avoided, and continuity and a complete topological structure of a long and narrow crack are ensured. According to the method, through collaborative optimization of multi-scale feature extraction and an attention mechanism, accurate capture of the saliency features of the crack and effective suppression of complex background interference are realized, and the detection sensitivity and overall segmentation consistency of the micro-crack are remarkably improved.

Three-dimensional outdoor positioning can be considerably enhanced by using various 3D imaging techniques, LiDAR ranging measurements of surrounding landmarks, and Inertial Measurement Units (IMUs) reaching accuracies in the centimetres range. These technologies, in conjunction with global navigation satellite systems (GNSS) such as GPS, GLONASS, Galileo, and BeiDou, provide autonomous geo positioning. It is crucial to acknowledge the constraints of dead reckoning, which may result in mistakes in positional precision. Integrating modern imaging and correction techniques can significantly improve the accuracy and dependability of outdoor localisation. The amalgamation of various 3D imaging methodologies with LiDAR enhancements improves the comprehension and ranging of the environment, tackling the difficulties presented by GNSS constraints. The integration of IMUs with satellite systems ensures the provision of continuous traveling positioning data, even in instances of obscured or faulty satellite signals. This comprehensive approach improves outdoor location precision and expands the use of these technologies across other domains, such as autonomous navigation, robotics, and geospatial analysis, resulting in more effective and dependable positioning solutions. The hardware system consists of a swarm of sensors, including GNSS, INS, IMU, LiDAR, stereo imaging, and RGB technologies, which are combined to generate three-dimensional colored representations of natural landscapes and urban environments. This system reconstructs topographical features, enabling detailed mapping and analysis. It produces comprehensive three- dimensional sensory data, including geo-location and POI identification, and incorporates advanced image processing techniques, such as edge detection, for precise spatial awareness and environmental understanding. The system is designed with a compact form factor, making it easily integrated into wearable devices and autonomous vehicles. The integration of these technologies forms a sophisticated network of swarm intelligence, crucial for autonomous vehicle positioning and navigation. Particle swarm optimization (PSO) is used to address non-deterministic navigation, with different coupling fusion methods available. The Neuro-fuzzy PSO AI Positioning and Navigation System generates three-dimensional sensory data, providing detailed information about geographical distribution and dead reckoning areas.

The invention relates to the technical field of high-resolution optical remote sensing image processing, in particular to a remote sensing image building extraction method and system based on local-global features, and the method comprises the steps: obtaining a remote sensing image containing a building, and inputting the remote sensing image into a trained building extraction network model; the model adopts a double-branch remote sensing image building network fusing local-global features, and comprises a double-branch structure encoder, a cross-feature attention fusion module and a feature refinement enhancement module. Wherein the double-branch structure encoder is further divided into a CNN branch and a Transform branch, the CNN branch is responsible for extracting local features, the Transform branch is used for extracting global features, the cross-feature attention fusion module fuses the features extracted by the CNN branch and the Transform branch, the feature refining enhancement module further enhances the feature extraction capability of the network at a decoder end, and the feature extraction capability of the network at the decoder end is enhanced. Therefore, the building in the remote sensing image can be effectively extracted.

The invention discloses an infrared and visible light image fusion method based on a conditional diffusion model, and belongs to the technical field of image fusion. Compared with a current popular infrared and visible light image fusion algorithm, the algorithm is obviously improved in the aspects of color fidelity, texture detail maintenance and fusion quality, and feature extraction and fusion tasks are completed through combination of a conditional denoising diffusion implicit model and a multi-channel high and low frequency parallel fusion model; the problem that the denoising diffusion implicit model lacks a reference true value of a fusion result when executing an image fusion task is relieved, the unique advantages of the denoising diffusion implicit model in image processing are further shown, and the method is excellent in the aspects of high-quality image generation and mathematical interpretability. The optimization of the existing method and the upgrading of the existing technology are realized.

The invention discloses a strip steel surface defect detection method based on an attention mechanism and an improved UNet network, relates to the field of industrial image processing, and aims to solve the defects of a traditional detection method in defect positioning precision, multi-scale feature fusion and class imbalance. According to the technical scheme, an improved UNet network architecture is adopted, and the method specifically comprises the following steps: in a coding stage, designing a UELAN feature extraction module to improve parameter efficiency; a coordinate attention mechanism (CA) is introduced to enhance the focusing capability on a defect area; and capturing multi-scale high-level semantic features in combination with a cavity space pyramid pooling module (ASPP). In a decoding stage, a convolutional block attention mechanism (CBAM) is integrated, and a channel and spatial feature fusion are dynamically optimized. Besides, a combined loss function is put forward, focus loss and Dice loss are fused, the problem of boundary uncertainty is relieved in combination with a label fuzzification strategy, and category imbalance is coped with through weighted cross entropy. According to the invention, high-precision pixel-level segmentation of strip steel surface defects is realized.

The invention belongs to the technical field of image processing, and particularly relates to an object six-degree-of-freedom pose estimation method based on three-dimensional geometric information registration, which comprises a target detection module, a three-dimensional point cloud generation model and a geometric information registration pose estimation module. According to the method, geometric reconstruction, a geometric information high-precision registration technology and an object pose estimation task are combined, a geometric reconstruction module introduces object geometric feature extraction and feature decoding to realize deep understanding and modeling of an algorithm on object geometric features, the whole process accords with geometric disorder, and the accuracy of object pose estimation is improved. And the accuracy and scene adaptability of the algorithm are further improved. According to the method, accurate estimation of the six-degree-of-freedom pose of the object can be achieved, and powerful support is provided for the fields of industrial automation, robot vision, AR / VR, automatic driving, medical robots and the like.

The invention relates to a pipeline detection robot defect identification method based on image processing, which comprises the following steps: carrying out continuous image acquisition on the inner wall of a pipeline through image acquisition equipment on a pipeline robot to obtain image data of the inner wall of the pipeline; performing image preprocessing on the pipeline inner wall image data; pipeline defects in the pipeline inner wall image data after image preprocessing are detected, and image segmentation is carried out on a defect area; and carrying out feature extraction on the defect area, carrying out defect classification, and outputting an identification classification result. According to the invention, based on algorithms of image processing, artificial intelligence and machine learning, defects of cracks, corrosion, leakage and the like in the pipeline can be automatically identified, the limitation of manual detection is solved, automatic and intelligent detection is realized, the technical effects are mainly embodied in the aspects of improving the detection precision and efficiency and the ability to adapt to different pipeline environments, and the method is suitable for popularization and application. High application and popularization values are realized.

The invention relates to the technical field of image processing, and discloses an image recognition and analysis system based on AI. The system comprises a data acquisition module, a feature extraction module, a model training module, a multi-modal fusion module, a dynamic optimization module and the like. The method comprises the steps of collecting real-time image data by a multi-source sensor, extracting features by a cascade convolutional neural network, generating an adversarial network training model, integrating multi-source data by multi-modal fusion, optimizing feature vectors by an improved genetic algorithm, and constructing a classification decision tree. In addition, an anomaly detection module, a real-time reasoning module, a data enhancement module and a visualization module are further arranged. The system can accurately identify and analyze images, improve the model performance and generalization ability, meet the real-time requirement of edge computing equipment, generate an interpretable report to assist decision making, and have wide application prospects in the fields of security, medical treatment, automatic driving and the like.