4507 results about "Image pre processing" patented technology

The invention relates to the technical field of data processing, in particular to a decision-making method and system for multi-source information fusion. The method comprises the following steps: obtaining hyperspectral image data, visible light image data and infrared image data collected by an unmanned aerial vehicle, and collecting ground environment monitoring data collected by a ground sensor; the hyperspectral image data, the visible light image data and the infrared image data are subjected to image preprocessing, so that a standardized multispectral data set is obtained, and image preprocessing comprises geometric correction, radiation correction and atmospheric correction; and color and texture feature extraction is carried out on the visible light image in the multispectral data set, and appearance feature and growth density identification of crops is carried out so as to obtain morphological feature data. Through integrated analysis of multi-source data, abnormal growth of crops and potential pest and disease damage risks can be identified more accurately, so that engineers are assisted to make irrigation and fertilization decisions.

The invention discloses a transformer substation engineering construction drawing intelligent analysis and evaluation system based on image recognition, and relates to the technical field of transformer substation engineering construction informatization. The data processing center is in communication connection with a data acquisition layer, an image preprocessing layer, a feature extraction layer, an intelligent analysis layer, an evaluation decision-making layer and an application display layer, and all layers of architectures are in electric signal connection. According to the method, the improved convolutional neural network model is adopted, and an image preprocessing algorithm specially aiming at engineering drawing feature optimization is combined, so that the drawing recognition accuracy is remarkably improved compared with a traditional OCR technology, the manual proofreading workload is greatly reduced, a drawing element correlation analysis system based on a knowledge graph is constructed, and the engineering drawing recognition accuracy is improved. Key information such as equipment arrangement, line connection and size marking in the drawing can be automatically extracted and associated, and structural storage and intelligent retrieval of drawing content are achieved.

The invention relates to the technical field of optical detection, in particular to an automatic optical detection method and system based on gasket surface defects. The method comprises the following steps: carrying out parameter extraction on a to-be-detected gasket, obtaining inner and outer diameters, thickness and tolerance range information of the gasket, and obtaining gasket structure parameter data; matching, calibrating and storing the gasket structure parameter data according to a preset element parameter library to obtain a gasket element parameter library; performing surface scanning on the to-be-detected gasket by using a visual sensor to generate original image data; performing multi-channel optical imaging processing on the original image data, and performing illumination unevenness compensation by using a high dynamic range technology to obtain enhanced surface image data; and carrying out image preprocessing on the enhanced surface image data to obtain gasket surface detection image data. According to the method, the multi-channel optical imaging and advanced image processing technologies are adopted, the self-adaptive algorithm is combined, tiny defects can be accurately recognized, and the false detection rate and the omission ratio are remarkably reduced.

The invention provides an access control system based on face recognition and a recognition method, and the system comprises a multi-modal image collection subsystem which integrates a visible light camera, a depth camera and an infrared thermal imaging camera and is used for synchronously collecting a visible light image, a depth image and an infrared thermal imaging image of a face; the image preprocessing unit is used for carrying out corresponding preprocessing on the image; the feature extraction network module is used for extracting image features; the multi-modal feature fusion and alignment module is used for calculating a mutual information matrix by adopting a feature fusion strategy based on mutual information maximization and adaptive weighting, determining an adaptive weight and performing weighted summation, and performing feature alignment by applying a method based on canonical correlation analysis; and the face recognition classification and verification module verifies the image by adopting a multi-level fusion strategy. According to the method, the false recognition rate and the rejection rate are effectively reduced, the strong anti-cheating capability is achieved, and cheating attacks of various fake face means can be accurately recognized and resisted.

The invention relates to the technical field of surface detection, and discloses a surface detection method and system for a precision fastener, and the method comprises the steps: carrying out the multi-angle image collection and image preprocessing of the precision fastener, and obtaining an initial feature image set; three-domain feature extraction is carried out, and surface feature fingerprint vectors are obtained through calculation of a texture feature domain, a shape feature domain and a material feature domain; constructing a depth separable convolution structure and a feature pyramid structure to obtain a depth surface detection network; taking the pre-trained target detection model as a teacher network, taking the deep surface detection network as a student network, and obtaining a target surface detection model through calculation of a characteristic distillation loss function and a response distillation loss function; and inputting a to-be-detected precise fastener image into the target surface detection model, and obtaining category information and position information of surface defects through two-stage measurement recognition calculation. According to the method, the calculation efficiency is improved while the detection precision of the precise fastener is ensured.

The invention discloses a multi-field part size and appearance defect intelligent detection system, and relates to the field of image analysis. The system comprises an image acquisition module, an image preprocessing module, a feature extraction module, a defect identification and size measurement module, a data processing and analysis module, an automatic control module and a man-machine interaction module. According to the method, CNN and LBP, Hough transform and SIFT algorithms are fused, high-level semantics and bottom-level texture / geometric features are considered, 2304-dimensional fusion feature vectors are formed through feature splicing, the feature extraction integrity of parts in multiple fields is improved, texture detail features can be accurately extracted, geometric shape features can be accurately obtained, and the method is suitable for large-scale popularization and application. Meanwhile, the adaptive feature selection mechanism dynamically optimizes the feature combination according to the detection result, the problem of calculation redundancy is reduced, and the detection precision is ensured while the detection efficiency is improved.

The embodiment of the invention discloses a circuit board defect automatic detection method and system based on machine vision, which are used for improving the precision and efficiency of circuit board defect detection and repair, and the method comprises the following steps: obtaining a surface image data set of a target circuit board; performing image preprocessing operation on the surface image data set to generate a preprocessed image data set; inputting the preprocessed image data set into a pre-trained defect feature extraction model, and performing multi-level feature fusion processing on each piece of image data through the defect feature extraction model to generate a multi-dimensional defect feature set; determining a defect positioning information set of the target circuit board according to a matching degree calculation result between the multi-dimensional defect feature set and a preset defect feature template; and transmitting the defect positioning information set to a defect repair control terminal, and triggering the defect repair control terminal to generate a repair path planning instruction according to the defect positioning information set.

The invention relates to the technical field of agricultural knowledge base retrieval, and discloses an intelligent retrieval system for a knowledge base, which comprises an image preprocessing unit, an image adjusting unit, an image matching unit and a retrieval judgment unit, and is characterized in that a binary mask matrix is generated through image enhancement and pixel clustering, a modular entropy value is calculated in combination with parameters, and a basis is provided for feature extraction; multi-scale feature self-adaptive extraction is achieved through dynamic scale adjustment, feature distortion is avoided, feature points are evenly distributed through density dynamic adjustment and quality evaluation, high-quality feature points are screened, double screening of matching pairs is achieved by constructing a comprehensive evaluation mode, and the defects of an SIFT algorithm are overcome by dynamically adjusting a similarity threshold value and sorting. And the accuracy of image retrieval in a complex scene is improved.

The invention relates to the field of remote sensing image processing, in particular to a high-resolution remote sensing image semantic segmentation method based on multi-scale feature fusion, which comprises the following steps: acquiring a public remote sensing image data set, preprocessing the image, and constructing a training and testing set of semantic segmentation; a CTMFNet is designed, an encoder is composed of a lightweight residual module and an MS-Transform, and local space details and global context information are extracted; rID is adopted to reduce spatial information loss, LSFE is introduced to improve spatial positioning capability, and feature calibration is carried out in space and channel dimensions through DecoderAttn to realize boundary fine segmentation; inputting the training sample into the network for training to obtain a converged optimal semantic segmentation model; and inputting the test set into the model to obtain a semantic prediction map, and outputting a fine segmentation result of the remote sensing image through multi-scale fusion and boundary restoration. According to the method, the precision and robustness of ground feature extraction are effectively improved, the calculation cost is remarkably reduced while high segmentation precision is kept, and the method has good practical value and popularization prospects.

The invention discloses a soil fertility intelligent assessment method and system based on image recognition, and relates to the technical field of data processing, and the method comprises the steps: obtaining land image information of a to-be-assessed region, the land image information comprising a multispectral image, a thermal imaging image and an RGB image of the to-be-assessed region; carrying out image preprocessing on the land image information, and carrying out vegetation coverage and health assessment according to the multispectral image: speculating soil characteristics according to the RGB image: speculating the humidity change of a soil surface layer according to the temperature distribution of a thermal imaging image, and identifying a drought region and a region with sufficient moisture; integrating the high-resolution texture features of the RGB image, the spectral information of the multispectral image and the speculation result of the temperature distribution of the thermal imaging image to generate a comprehensive analysis result; and classifying soil types and evaluating fertility grades according to a comprehensive analysis result. The problem that in the prior art, in intelligent soil fertility evaluation, workers need to carry out on-site sampling examination is solved, and the technical effect of improving the soil fertility evaluation efficiency is achieved.

The invention discloses an artificial board surface defect intelligent detection method and system based on machine vision, and particularly relates to the technical field of artificial board surface defect detection. An artificial board surface image is collected, image preprocessing, feature extraction, defect segmentation and classification recognition are carried out through a deep learning algorithm, a multi-scale convolutional neural network is adopted to carry out feature extraction on the image, a shallow convolutional layer captures small defect details, a deep convolutional layer recognizes global features of large defects, and a multi-scale convolutional neural network is adopted to carry out feature extraction on the image. Accurate defect segmentation is carried out through a Mask R-CNN model, a redundant frame is removed in combination with a non-maximum suppression algorithm, the classification problem of adjacent defects is corrected by using an error correction algorithm in combination with the spatial relationship and morphological characteristics of the defects, and defect information is fed back to a production line control system in real time; and defective products are automatically removed or production process parameters are automatically adjusted, so that the automation level of a production line is effectively improved, the product quality is optimized, and human intervention and production cost are reduced.

The invention provides a fire early warning and intelligent fire extinguishing method based on image recognition. The fire early warning and intelligent fire extinguishing method comprises the steps that a camera network is used for covering a target monitoring area, flame and smoke characteristic parameters are input, noise is removed through image preprocessing, and image data are standardized. A heat source point is selected as a camera installation position in combination with a fire propagation mode, and layout is optimized. And calling image data, performing real-time analysis based on a dynamic flame sequence, extracting abnormal response, calculating a fire risk value by using a mode recognition algorithm, and generating peak fire probability data. And collecting real-time fire characteristic data, comparing the data with a risk value after filtering and noise reduction, and setting multi-level threshold values to generate an alarm result. And analyzing a false alarm reason, optimizing the position of the camera and the starting condition of the fire extinguishing device, and generating a fire risk and fire extinguishing efficiency report. According to the invention, the accuracy and timeliness of fire early warning can be improved, the false alarm rate is reduced, and the fire extinguishing efficiency is enhanced.

The invention discloses a reservoir bank slope crack identification method based on unmanned aerial vehicle inspection and digital images, and relates to the technical field of reservoir bank slope inspection. 2, image preprocessing: preprocessing the data by adopting processing methods of graying, gray enhancement and bilateral filtering; 3, crack identification: identifying reservoir bank slope cracks in the image by adopting methods of threshold segmentation, edge detection and supervised classification; 4, crack pixel filtering: performing filtering processing on the extracted crack image by adopting morphological closed operation, length filtering and direction filtering; and 5, model fusion: fusing the threshold segmentation model, the edge detection model and the supervised classification model together by adopting a minimum-risk Bayesian probability method. The method shows remarkable applicability and effect in large-range and multi-form crack identification and investigation, and shows high robustness when processing crack images with different scales, shapes and backgrounds, and the accuracy of the method can stably reach more than 90%.

The invention discloses a bridge crack identification and automatic evaluation method based on image identification and AI modeling, and the method comprises the following steps: S1, obtaining an original image of a bridge structure surface, and carrying out the image preprocessing; s2, inputting the standardized image into an image recognition model, performing pixel-level segmentation on a crack region in the image, and outputting a crack mask graph; s3, performing feature extraction processing on the crack mask graph, extracting geometric feature parameters of the crack, and constructing a crack feature vector; s4, constructing an evaluation model based on a supervised learning method, and training the evaluation model; and S5, inputting the crack feature vector into an evaluation model, evaluating the structural risk level of the crack, and outputting a structural risk label. According to the method, image recognition and AI modeling are fused, automatic crack recognition and evaluation are achieved, and the method has the advantages of being high in precision, clear in boundary and intelligent in evaluation.

The present disclosure provides a multi-task panoptic driving perception method and system based on improved You Only Look Once version 5 (YOLOv5). The method in the present disclosure includes: performing image preprocessing on an image in a dataset to obtain an input image; extracting a feature of the input image by using a backbone network of improved YOLOv5, to obtain a feature map, where the backbone network is obtained by replacing a C3 module in a backbone network of YOLOv5 with an inverted residual bottleneck module; inputting the feature map into a neck network to obtain a feature map, and fusing the obtained feature map and the feature map obtained by the backbone network; inputting the fused feature map into a detection head to perform traffic target detection; and inputting the feature map of the neck network into a branch network to perform lane line detection and drivable area segmentation.

The invention discloses an ROV positioning navigation system based on vision inertial navigation fusion and pose correction, which comprises an inertial measurement value and image preprocessing module loaded on an ROV, the inertial measurement value and image preprocessing module is connected with a vision and inertial navigation data fusion module through a wire, and the vision and inertial navigation data fusion module is connected with a pose correction module. The invention also discloses a positioning navigation method of the ROV positioning navigation system based on visual inertial navigation fusion and pose correction. According to the method, the inertial data provided by the IMU and the environment characteristic information captured by the visual sensor are fused, high-precision estimation of the position and attitude of the ROV can be realized, the sensor data are optimally fused by using the MSCKF algorithm, the influence of the error of a single sensor on the positioning result is reduced, and the positioning precision and reliability of underwater operation are remarkably improved.

The invention relates to the technical field of PCB welding spot defect detection, and discloses a PCB welding spot defect detection system and method based on image recognition, and the system comprises an image preprocessing module which is used for obtaining an original image flow and dividing an interested detection area; the feature fusion module is used for extracting multi-modal features to form a fusion set; the defect judgment module is used for establishing a mapping index and obtaining a judgment result; the parameter calibration module is used for verifying the detection parameters and adjusting the mapping index; and the report output module is used for generating a defect detection report. The method comprises the steps of image preprocessing, feature fusion, defect discrimination, parameter calibration, report generation and the like. According to the system and the method, the PCB welding spot defects can be efficiently and accurately detected, the detection precision and stability are improved, a structured report is generated, and an effective solution is provided for PCB quality detection.

The invention relates to the technical field of printed circuit board design, in particular to a printed circuit board defect detection method and system based on image recognition. The method comprises the following steps: carrying out image preprocessing on an original PCB image to obtain a preprocessed PCB image; performing scale feature fusion on the preprocessed PCB image to obtain a multi-scale feature map; performing background texture feature learning on the multi-scale feature map to obtain a background suppression feature map; performing background texture suppression according to the background suppression feature map to obtain a significant defect map; and performing local statistical feature calculation according to the pre-processed PCB image and the saliency defect graph to obtain a local statistical feature graph. Through a series of technical means of multi-scale feature fusion, background texture feature learning and suppression, adaptive threshold segmentation and morphological optimization, the problem of complex background texture interference is effectively solved, and the accuracy and robustness of PCB defect detection are improved.

The invention discloses an automobile steering controller fault detection method and system, and relates to the technical field of automobile electronic control. The surface image acquisition module captures an image through a visual sensor, the quality is improved through the image preprocessing module, the convolutional neural network accurately recognizes appearance defects, and the three-dimensional contour detection and thermal imaging module is triggered to be linked to re-check an abnormal area; the three-dimensional contour module confirms patch offset or tombstone standing abnormity by using a laser scanning technology; the thermal imaging analysis module is matched with a machine learning technology to analyze welding spot temperature abnormity; the predictive fault diagnosis module predicts a defect risk by using deep learning and performs early warning in advance; and the collaborative decision optimization module integrates a multi-module data dynamic optimization detection strategy. Through integration of defect detection, re-checking and prediction, the quality detection precision, coverage and efficiency of the automobile steering controller are greatly improved, the high-quality standard and production stability of products are ensured, and the requirement of the automobile industry for efficient detection is met.

The invention provides a self-adaptive nonlinear image enhancement method and system for a low-light scene of a mobile terminal, and relates to the technical field of image enhancement, and the method comprises the steps: carrying out the image preprocessing and noise reduction, and carrying out the graying and noise suppression of an input color image through a local variance self-adaptive algorithm; adaptive down-sampling is carried out, and the down-sampling proportion is dynamically adjusted according to the image resolution and the content complexity, so that the processing efficiency is improved; brightness adaptive enhancement is carried out, and the overall brightness of the image is rapidly improved by adopting an Otsu method and a lookup table; contrast nonlinear enhancement: enhancing image details and contrast in combination with a Laplace operator and local mean adjustment; and color restoration: restoring the resolution through bilinear interpolation and performing weighted fusion to realize natural color reconstruction. And finally, a high-quality image of which the brightness, the contrast ratio and the color are remarkably improved is output. According to the invention, the recognition accuracy and processing efficiency of the low-illumination image are improved.

The invention relates to the technical field of green plant image recognition, in particular to a green plant disease recognition and early warning management system and method. The method comprises the following steps: acquiring a high-definition image of a green plant; performing image preprocessing on the green plant high-definition image to generate a standard green plant high-definition image; performing green plant part image segmentation and integration on the standard green plant high-definition image to obtain a standard green plant part image; performing part multi-dimensional disease feature extraction on the standard green plant part image to obtain green plant part multi-dimensional disease features; performing scab area identification on the standard green plant part image based on the multi-dimensional disease features of the green plant part to generate scab area identification data of the green plant part; and performing green plant disease type discrimination on the standard green plant part image according to the green plant part scab area identification data to generate green plant disease type discrimination data. According to the method, high-definition image processing, multi-dimensional feature extraction, propagation trend analysis and dynamic early warning are introduced, so that the accuracy and reaction time sequence of green plant disease recognition early warning are improved.

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 agriculture, in particular to a mulberry field monitoring method and system based on the Internet of Things. The method comprises the following steps: acquiring environment and soil data of a mulberry field through a sensor network to obtain original sensor data; performing time sequence data integration on the original sensor data to obtain an environment parameter data set; the method comprises the following steps: performing multispectral image acquisition on mulberries in a mulberry field through a multispectral camera, and performing image preprocessing to obtain preprocessed image data; estimating the chlorophyll content according to the preprocessed image data to obtain chlorophyll content data; and performing growth health parameter analysis according to the preprocessed image data and the chlorophyll content data to obtain growth health parameters. By combining the sensor technology, the multispectral imaging technology and the intelligent algorithm, comprehensive, accurate and real-time monitoring of the mulberry field is achieved, and an effective technical means is provided for fine management of the mulberry field.

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 provides a mountainous area tunnel construction safety intelligent monitoring and early warning method and system, and relates to the technical field of construction safety monitoring, and the method comprises the steps: collecting visible light and depth images of tunnel surrounding rock, and carrying out the segmentation and extraction of crack features through a depth attention network after image preprocessing and data fusion; extracting parameter time sequence data based on the crack spatial position and the type feature; determining fracture evolution characteristics and critical state parameters by combining wavelet transform and stress-rate coupling analysis; and adopting deep reinforcement learning to calculate the instability probability and generate early warning information. According to the invention, intelligent identification, instability prediction and risk early warning of tunnel surrounding rock cracks are realized, and the safety monitoring accuracy and early warning timeliness are improved.

The invention provides an image restoration and super-resolution reconstruction system and method based on deep learning, and belongs to the technical field of digital image processing. The invention aims to solve the problems of high calculation complexity and resource consumption, limitation of long sequence processing, high training difficulty and texture scene deficiency when a multi-scale residual network based on a Transform architecture is used for image resolution conversion. The reconstruction system comprises: an image preprocessing module performing window division and video memory optimization on an input low-resolution image; the multi-layer fusion network dynamically adjusts the characteristics of the low-resolution image, captures channel information in different scenes, performs interactive fusion, performs comparison supervision, establishes an information communication channel, dynamically adjusts and optimizes parameters through negative feedback, and obtains a super-resolution image. And the loss function module maximizes the similarity of the super-resolution image and the high-resolution image in the segmentation feature space to obtain a final super-resolution image.

The invention discloses an SMT welding spot defect detection method based on image data. The method comprises the following steps: S1, acquiring an image of an SMT welding spot area; s2, carrying out image preprocessing; s3, carrying out welding spot region segmentation by adopting a self-adaptive region growing algorithm, and carrying out modeling on a welding spot region contour in combination with a boundary fitting optimization algorithm to generate a welding spot contour model; s4, multi-level comprehensive features are extracted from the welding spot area, and the multi-level comprehensive features are constructed into a feature vector set; and S5, inputting the feature vector set into a welding spot defect detection network, predicting a defect mode existing in the current batch of welding spots by adopting an improved ProtoNet model, and completing classification and identification of welding spot defects by adopting adaptive feature filtering. According to the method, the adaptive region growing algorithm and the improved ProtoNet model are adopted, accurate detection of the welding spot defects is achieved, and the method has the advantages of being high in detection precision, high in adaptability and good in robustness.

The invention discloses a multi-modal semi-supervised medical image segmentation method based on SAM adaptive fine tuning, and the method comprises the steps: obtaining an original multi-modal image, adjusting the original multi-modal image to a preset size, and carrying out the image preprocessing, thereby obtaining a multi-modal image pair; simultaneously inputting the multi-modal image pair into a student branch model and a teacher branch model which are completely identical in structure, wherein each of the student branch model and the teacher branch model comprises a fusion module, an SAM encoder and a layered decoder; the multi-modal image pair passes through a fusion module formed by a convolutional network to obtain fusion features, the fusion features are input to the SAM encoder, and output features of different Transform layers are obtained; and inputting the plurality of output features into a hierarchical decoder, performing similarity calculation by the hierarchical decoder according to the plurality of output features to obtain prototype classification, and obtaining a prediction mask through a linear layer. According to the method, the multi-level features can be well utilized to realize fine-grained segmentation, and a good segmentation effect is realized.

The invention provides a medical image segmentation method based on geometry and topology cooperative guidance. The medical image segmentation method comprises the following steps of image preprocessing and data enhancement; a shared encoder; a dual-path cooperative decoder; carrying out multi-mode deformation iterative refining; and a multi-objective composite loss function and an optimization strategy. The method has the beneficial effects that the performance can be remarkably improved: through a unique geometry and topology collaborative refining mechanism, the segmentation precision and the boundary definition are far superior to those in the prior art, the topology correctness of an anatomical structure can be actively maintained and repaired, clinically unacceptable errors are remarkably reduced, and the reliability of a result is improved; in addition, operation can be simplified, stability and generalization are enhanced, and advanced application is promoted.

The invention discloses a water area surface flow velocity area monitoring method based on intelligent visual analysis, and relates to the technical field of intelligent visual analysis. According to the specific implementation scheme, continuous image collection is conducted on a target water area, dynamic images flowing on the surface of the water area are obtained, and image preprocessing is conducted on video frames of the dynamic images; directly comparing global feature similarities through feature extraction, feature matching and optical flow optimization by using an optical flow estimation algorithm of a convolutional neural network, and outputting optical flow field data of the water area surface flow velocity; after the optical flow field data are obtained, pixel displacement information of optical flow estimation is converted into a flow velocity field on an actual physical unit, and meanwhile regional distribution of the flow velocity on the surface of the water body is analyzed. According to the invention, the optical flow estimation algorithm of the convolutional neural network is utilized to directly compare global feature similarities and output water area surface flow velocity data.