4513 results about "Edge detection" patented technology

The invention provides a circuit board welding fault identification method and system based on machine vision, and relates to the technical field of visual identification, and the method comprises the steps: obtaining an image of a circuit board welding region through employing a structured light projection system and a multi-camera imaging system; realizing accurate positioning of a defect region in the region of interest by utilizing three-dimensional shape information and through morphological operation and an edge detection algorithm; for the accurately positioned defect area, obtaining high-precision three-dimensional point cloud data of the defect area, extracting a multi-scale curvature feature descriptor, and fusing the multi-scale curvature feature descriptor with visual features to form a multi-feature descriptor; inputting the multi-feature descriptors into a defect classification and recognition module based on ensemble learning to obtain the types and severity of the welding defects; and according to a defect identification result, the three-dimensional space distribution and severity of the welding defects are visually displayed.

The invention relates to the technical field of the Internet of Things, and discloses a gas leakage monitoring method, device and equipment based on the Internet of Things and a storage medium, and the method comprises the steps: carrying out the data collection of a gas storage device, and obtaining an initial multi-dimensional data set; performing filtering and edge detection processing to obtain potential abnormal point data; inputting the potential abnormal point data into a mixed deep learning model for gas leakage detection to obtain initial leakage source position data and a leakage intensity value; executing multi-sensor cooperative accurate leakage positioning to obtain target leakage source position data; performing gas diffusion simulation to obtain gas concentration distribution data and risk indexes; based on the gas concentration distribution data and the risk index, dynamic reconstruction of the sensor network is carried out, an emergency response strategy and sensor network configuration information are output, and the method realizes whole-process management from leakage detection and source positioning to risk assessment and emergency response.

The invention relates to the technical field of image processing, and discloses an industrial article appearance detection method and system based on machine vision. The method comprises the steps that a workpiece image is collected, and a standardized image is obtained through Gaussian filtering, histogram equalization and Otsu algorithm preprocessing; feature vectors are extracted through edge detection, Hough transform and a gray-level co-occurrence matrix; training a defect identification model by adopting a CNN of an attention mechanism; inputting an image for real-time detection; fusing the detection data and the environmental parameters by using a D-S theory; and establishing a database based on a distributed architecture to generate a quality report. According to the method, the detection accuracy is improved through multi-source information fusion, and efficient management and analysis of mass detection data are realized by adopting a distributed architecture.

The invention provides a textile defect positioning method, which comprises the following steps: according to an extracted defect feature map, fusing feature maps of different scales, extracting multi-scale defect features, and comprehensively depicting space distribution and combination information of defects; aiming at the condition that multiple flaws exist at the same time, a multi-task learning model is constructed, flaw detection, classification and positioning tasks are carried out at the same time, and a back propagation algorithm is adopted to optimize the multi-task learning model; positioning the defect area by adopting an image segmentation algorithm, obtaining the contour of the defect area according to the positioning result of the defect area and by adopting a Canny edge detection algorithm, and calculating the area and position information of the defect; and based on an evaluation result, a dynamic threshold adjustment algorithm is adopted, a defect judgment threshold is adjusted according to a production batch and a product type, and a threshold parameter is dynamically updated by analyzing historical data and a current production condition.

The invention discloses an unhooking robot dynamic unhooking control method and system based on a feedback mechanism, and relates to the technical field of robot control, and the method comprises the steps: obtaining an unhooking robot, moving the unhooking robot to a preset unhooking position, and starting a visual sensor to collect unhooking image information and environment perception image information; edge detection and feature extraction are carried out on the unhooking image information, and hook space coordinate information is determined; performing feature abstraction on the environment perception image information to obtain a target unhooking path; and dynamic characteristic parameters and motion constraint information of the unhooking robot are obtained, unhooking analysis is carried out, unhooking control parameters are obtained, and dynamic unhooking control is carried out. The technical problems that in the prior art, an unhooking robot is low in unhooking efficiency and insufficient in control precision, and certain safety risks exist are solved, and the technical effects that the unhooking efficiency and the control precision of the unhooking robot are improved, and operation safety is guaranteed are achieved.

The invention relates to the technical field of image processing, and discloses a stranded wire production quality monitoring method and system based on machine vision. The method comprises the following steps: acquiring the surface of a stranded wire at multiple angles through a high-speed camera array to obtain an original image; filtering high reflection and noise to obtain a feature map; performing edge detection, segmenting and identifying defects, and generating a position mapping table; quantitatively calculating defect parameters to form a quality score; performing correlation analysis to adjust process parameters; and constructing a defect-process database to realize quality tracing early warning. According to the invention, full-process automatic control from defect identification, quantitative evaluation to process parameter optimization is realized, the defect-process relational database is constructed, and the tracing and early warning functions of production quality are supported, so that the quality stability and the intelligent level of stranded wire production are improved.

The invention discloses an electric power system abnormal remote signaling detection method based on a graph auto-encoder model, and the method comprises the steps: collecting measurement data of an electric power system, carrying out the preprocessing, obtaining a graph data set with an abnormal label, and dividing the graph data set into a training set and a test set; inputting the graph data in the training set into the graph auto-encoder model for training; after training is completed, abnormal score distribution is counted based on normal edge samples in a training set, a threshold value is set to serve as a follow-up judgment basis, and threshold value selection takes the accuracy rate and the recall rate on a test set as an adjustment and optimization target; in a test stage, image data in a test set are input to carry out edge feature reconstruction and anomaly scoring, anomaly judgment is carried out on edges in combination with a set threshold value, and a preliminary abnormal edge detection result is output; and the output abnormal edge detection result is input into the graph restoration module, the restored edge structure and edge features are output, the damaged remote signaling state in the power grid is restored, and the integrity of the graph structure and the operation credibility of the power system are improved.

The invention relates to the technical field of typesetting and drawing, in particular to an automatic typesetting and drawing method and system for lens laser cutting. The method comprises the following steps: collecting an image of a to-be-cut lens, carrying out contour edge detection, generating a contour direction sequence, carrying out polygon fitting on the sequence, screening out effective contour data, constructing a three-dimensional lens model, carrying out classification clustering on the model to extract contour features, carrying out segmentation to obtain a segmented contour model, and carrying out image segmentation on the segmented contour model. The method comprises the steps of obtaining a model, carrying out range limitation and local typesetting processing on the model to form an accurate layout scheme, constructing a cutting path direction chain according to the scheme, carrying out path planning, generating cutting path data, carrying out sequential execution setting on cutting paths based on a preset blank drawing template, and finally obtaining a typesetting template for laser cutting. The automatic typesetting and drawing method for lens laser cutting is more efficient.

The invention relates to the technical field of computers, and discloses a cultivated land resource quality grade evaluation management method and system, and the method comprises the steps: carrying out the spectrum residual error normalization processing of a multi-temporal remote sensing image, generating a corrected image sequence, effectively inhibiting the reflectivity difference caused by the alternation of wetting and drying of a water body, and achieving the evaluation of the quality grade of cultivated land. A first boundary result and a second boundary result are respectively extracted by using depth segmentation and progressive edge detection, the adaptability and robustness of boundary extraction are enhanced, a difference graph is further generated based on a complex phase difference matrix, the second boundary is corrected, a coherent boundary result is obtained, the boundary time sequence consistency is remarkably improved, and the robustness of boundary extraction is improved. And finally, the cultivated land parcel units are constructed based on the coherent boundaries, and the random forest model is input to complete scoring and grading, so that the stability and traceability of the cultivated land quality evaluation result under multiple temporal conditions are ensured, and the technical targets of stably extracting the boundaries and accurately completing grading in a dynamic change scene are achieved.

Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.

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 de-noising diffusion model driven texture enhanced infrared and visible light image fusion method and system, and relates to the technical field of image processing and computer vision. The method comprises the following steps: performing multi-scale space-time modeling on infrared and visible light images; enhancing high-frequency texture features of the infrared image through an edge detection convolutional network; based on a bidirectional multi-scale convolution module and a bidirectional attention fusion module, capturing a remote dependency relationship and encoding multi-scale information to ensure full utilization of global information and accurate capture of local details; and optimizing the structural fidelity and color intensity distribution of the fusion result based on an adaptive structural similarity loss function and multi-channel intensity and texture loss so as to improve the quality of the fused image. According to the method, the process of diffusion feature extraction is enhanced by fusing the de-noising diffusion model and the texture enhancement strategy. Meanwhile, a bidirectional multi-scale convolution technology and an attention mechanism are combined, so that comprehensive utilization of global information and accurate mastering of local details are ensured.

The invention discloses an efficient 3D Gaussian scene reconstruction method based on multi-modal depth distribution supervision, and compared with the prior art, pixel-level depth dense point clouds are directly extracted in a Gaussian initialization stage, a multi-modal depth distribution map is generated, random initialization of dense Gaussian positions according to COLMAP sparse point clouds is effectively avoided, and the reconstruction efficiency is improved. The problems that under the condition of few views, point clouds obtained by COLMAP are too sparse, provided geometric constraints are insufficient, and meanwhile randomly initialized Gaussian blocks the point clouds to move to the correct position due to the local minimum value are solved. According to the method, an image segmentation algorithm based on edge detection is applied to the image frame, a large number of Gaussian primitives which are far away from edge points and are similar in depth are fused, the 3D Gaussian number is effectively pruned, and memory consumption of a GPU is saved. According to the method, extra isotropic regularization loss is introduced into global gauss, so that the problem of Gaussian distribution in which artifacts are generated along the line-of-sight direction and the height is lengthened is avoided.

The invention provides a metal material defect identification and analysis method and system based on a multiple detection technology, and relates to the technical field of metal defect detection, and the method comprehensively utilizes microscope detection and ultrasonic detection technologies to obtain surface topography data and internal structure data of a target metal material. And performing feature alignment and fusion processing on the two types of data through a multi-grid method to generate a unified feature distribution model, and establishing a mathematical solution model of ultrasonic wave field propagation based on the model. And determining the position, shape and size information of the potential defect by using the solved data, and generating defect positioning data containing three-dimensional coordinates and contour features. And extracting key defect information in combination with an edge detection algorithm, judging defect types by adopting a classification algorithm, and generating a detection result. According to the method, the problems of insufficient detection precision, high data fusion difficulty and low classification efficiency in the prior art are solved, and the applicability and reliability of a defect detection technology are improved.

The invention provides a water area safety patrol method and system based on multi-modal feature analysis, and relates to the technical field of image recognition. The method comprises the steps of collecting a visible light image, an infrared image and laser radar data corresponding to a target water area, and performing time synchronization and space alignment to obtain visible light alignment data, infrared alignment data and laser radar alignment data; in response to the condition that the definition of the visible light image is higher than a quality reference value, carrying out edge detection and region segmentation processing on the visible light alignment data, and generating a candidate region set containing a target contour; for the infrared alignment data corresponding to the candidate region set, screening out a thermal anomaly region from the candidate region set; and carrying out three-dimensional space feature extraction and motion state feature extraction on the laser radar alignment data corresponding to the thermal anomaly region, and carrying out anomaly identification on the thermal anomaly region. According to the scheme, high-precision safety patrol of the target water area in various environments can be realized.

The invention discloses an intelligent positioning method and system for can making stamping, and relates to the technical field of image processing. Comprising the following steps: acquiring a tank stamping part image, and segmenting the tank stamping part from a background by adopting a self-adaptive threshold segmentation algorithm to obtain segmented tank region image data; utilizing a moment-based sub-pixel edge detection method to accurately position the edge coordinates of the contour of the tank body, and obtaining the key dimension parameters of the tank body; extracting texture features of the surface of the tank body by adopting a gray-level co-occurrence matrix and a local binary pattern algorithm, and judging whether the texture features belong to surface defects or not through a support vector machine classification model; the method solves the problem that edge information is difficult to accurately extract when a traditional image processing method faces tank surface reflection and a complex background, ensures the accuracy of a measurement result by establishing a tank coordinate system and performing attitude correction, overcomes the problem that the tank is easy to deviate or incline in the production process, and improves the production efficiency. And high-efficiency and accurate quality detection of the tank stamping part is realized.

The invention relates to the technical field of knitted fabric defect detection, in particular to a knitted fabric defect detection method and system based on machine vision, and the method comprises the steps: firstly obtaining a first image set through collecting the production process data of a scarf; constructing a scarf multi-scale edge detection model to identify the first image set, dynamically adjusting a threshold according to the actual brightness and gradient distribution of the image, and generating a first edge feature set; gradient information fusion is carried out on a background dynamic image and the first edge feature set, a scarf region segmentation model is constructed, a scarf production real-time image is segmented, and a time sequence segmentation image set and scarf flaw edge data are obtained; performing real-time analysis on the scarf flaw edge data and the time sequence segmentation image set to obtain scarf time sequence edge flaw track data; and constructing a scarf flaw edge complexity evaluation model to analyze the scarf time sequence edge flaw trajectory data, obtaining the fractal dimension of the scarf flaw edge, and generating the complexity of the scarf flaw edge.

Disclosed in the present invention are a method, apparatus and device for detecting an electrode pole defect of a lithium battery, and a storage medium. The method comprises: acquiring a depth map and a 2D grayscale map of an electrode pole, wherein the size of the depth map is the same as that of the 2D grayscale map; performing equalization processing on the depth map to obtain a first image; performing filtering processing and edge detection on the first image to obtain an electrode pole region; according to an edge profile coordinate point of the electrode pole region, locating a detection position corresponding to the 2D grayscale image, so as to obtain a detection region; and detecting different types of defects in the detection region by means of a preset defect detection algorithm, so as to obtain a defect detection result. The detection quality can be improved, the detection speed can be ensured, the requirements of the industry for electrode pole inspection of lithium batteries can be satisfied, and the quality inspection requirements of the new energy industry for electrode pole inspection of lithium batteries can be met.

The invention discloses a virtual water gauge water level identification method based on a multi-mask matching segmentation network. The method comprises the steps that S1, a real-time image of a target water body area is acquired through a preset position camera; S2, a virtual water gauge geometric model is constructed; s3, extracting a region with high confidence as a candidate region of the segmentation model; s4, designing a new training data structure optimization method to help the model better understand and utilize information of different levels; s5, respectively carrying out maximum matching on the rows and the columns, finding out an optimized matching path with the minimum total cost, and obtaining a finer segmentation result; s6, identifying a water body boundary in the high-precision water body segmentation map through a Canny edge detection algorithm, and extracting a waterline position; and S7, calculating an actual water level value, and for the water body with dynamic movement, smoothing the water level reading by adopting an interval time sampling moving average method. The defects of a traditional physical water gauge and an existing image processing technology are overcome, and an innovative solution is provided for water level monitoring.

The invention provides a medical image segmentation method, which comprises the following steps: acquiring PET indirect development original image data, and preprocessing the image, including denoising, contrast enhancement and gray level normalization, to obtain a preprocessed PET image data set; designing segmentation middleware, integrating a plurality of segmentation algorithms including threshold segmentation, region growth and level set methods, and selecting proper segmentation algorithms for different types of lesion regions according to feature vector analysis results; pET image data with labels are generated through the simulation technology, a training data set is constructed in combination with real case data, the sample size is expanded through the data enhancement technology, and a large-scale data set used for algorithm training is generated; and the segmentation result of the U-Net is refined by using the segmentation middleware, the lesion area edge is focused on, and the lesion boundary is accurately positioned through an edge detection algorithm to obtain a fine segmentation result.

The invention discloses a double-branch edge detection method and system based on CNN and Transform deep interactive fusion, and relates to the technical field of computer vision and deep learning, and the method comprises the steps: extracting multi-scale local features through a CNN-based fine semantic edge branch by employing an OfficientNet-B2 backbone network, and carrying out the deep supervision in combination with a side output structure; meanwhile, through a global context branch based on lightweight Transform, an efficient local attention mechanism is adopted to capture a long-distance dependency relationship of the image; the core of the invention lies in that a cross-attention module is designed and realized, local features extracted by a CNN branch are used as a Key and a Value, and features of a Transform branch are used as Query, so that deep interaction and fusion of the two branches on the feature level are realized, instead of simple feature splicing; in addition, the method also adopts a uniform loss function for balancing dynamic weighted combination of binary cross entropy loss, focus loss, Dice loss and the like to perform model optimization.

The invention relates to the technical field of image processing, in particular to an anti-collision beam weld defect detection method and system based on image processing, and the method comprises the steps: carrying out the image collection of a weld region of a produced anti-collision beam, and obtaining a gray image; the method comprises the following steps: performing initial partitioning on a grayscale image, respectively obtaining a local complexity index of each initial sub-block, obtaining at least two adaptive sub-blocks based on the local complexity index of each initial sub-block, and performing adaptive local histogram equalization on each adaptive sub-block to obtain a target grayscale image; the method comprises the steps of performing edge detection on a target grayscale image to obtain at least two edge pixel points, performing frequency domain conversion on the target grayscale image according to a gradient direction of each edge pixel point to obtain a frequency domain image, performing filtering processing and time domain conversion on the frequency domain image to obtain a denoised image, and identifying defects in the denoised image by using a neural network. And the defect detection efficiency is improved by inhibiting the periodic texture in the weld seam image.

The invention discloses a plant disease and insect pest early warning method and system based on plant monitoring, and belongs to the field of plant disease and insect pest early warning, and the method comprises the steps: extracting the contour of a lesion region through an edge detection algorithm, carrying out the smoothing of the contour through the combination of morphological operation, and obtaining a more precise lesion region boundary; according to an image segmentation result and a disease type identification result, a plant health condition evaluation model is established, and the plant damage degree is quantified; environmental data and image data are acquired from a plurality of sensor nodes distributed in a field, and the data are gathered to a regional gateway through wireless transmission; carrying out preprocessing and feature extraction on the converged heterogeneous data in a regional gateway, removing noise data and redundant information, and extracting key features; and carrying out pest detection and counting on the preprocessed image by using a deep learning model, carrying out modeling on a pest number change trend, predicting population density change in a period of time in the future in combination with environmental factors, and generating a detection result.

The invention relates to the technical field of image processing, and discloses a precast concrete T-beam bubble detection method and system. The method comprises the following steps: carrying out multiband reflection data acquisition and calibration on the surface of a T-beam, and obtaining optimized data through light intensity compensation and band screening; performing spatial registration and noise reduction processing on the data, and fusing the data into an image; edge detection and image segmentation are carried out, and pore morphological features are extracted; calculating depth data and performing spatial reconstruction to obtain pore distribution data; performing statistical analysis on the distribution data to obtain a defect evaluation result; and finally generating a detection report. According to the method, accurate positioning and quantitative evaluation of bubble defects are realized through multi-source data fusion and deep analysis, a complete early warning and evaluation mechanism is established, and the reliability and practicability of a detection result are 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 cover detection, in particular to a cover cutting piece quality detection method and system, and the method comprises the steps: carrying out the image collection of a cutting piece, and carrying out the preprocessing; according to the shape features and edge quality requirements of the cover, identifying the edge of the cutting piece based on a self-adaptive edge detection algorithm; constructing an edge defect identification model, and performing defect identification and classification on the detected edge contour to obtain an edge defect detection result; for defect types which cannot be determined by edge detection and defect identification, uploading the cut-part image data and the preliminary analysis result to a cloud to obtain a cloud analysis result; and generating a final quality detection result according to an edge defect detection result and a cloud analysis result. According to the invention, the problems of low manual detection efficiency, poor accuracy and incomplete defect identification in the traditional cut-part quality detection are effectively solved, the edges and defects of the cut-parts are accurately identified, the detection precision and consistency are improved, and the manual error is reduced.

The invention discloses a solar cell defect detection method based on a multi-mode sensing technology. The system comprises the following steps: 1, multi-modal data acquisition: acquiring multi-modal data in real time by using four sensing technologies of near-infrared laser, area array visible light, PL and EL; 2, multi-modal data processing: carrying out denoising, edge detection and feature extraction on the acquired near-infrared, visible light, PL and EL images by adopting a mixed image processing algorithm to obtain key defect features; 3, multi-modal data fusion: combining a multi-modal data fusion method with a decision-making level strategy, synthesizing different technical data, improving the detection precision, and classifying, positioning and labeling the defects of cracks, broken gates, scratches, depressions, poor welding and hot spots by using a convolutional neural network to ensure accurate distinguishing of surface and internal defects; and 4, defect marking and production line adjustment: marking defects and adjusting the production line through a feedback and automatic control module, and feeding back a real-time visual result to a management layer.

The invention relates to the field of excavation equipment obstacle avoidance based on image analysis, in particular to a coal mine excavation equipment obstacle avoidance method and system based on machine vision, and the method comprises the steps: obtaining the frequency domain characteristic parameters of the current electromagnetic noise during the operation of underground electromechanical equipment, and synchronously collecting the current original image frame output by a visual sensor; after a current de-noised image frame is obtained after dynamic de-noising processing, an obstacle contour feature point set is extracted through multi-scale edge detection, and an obstacle position confidence map is obtained according to space-time consistency analysis; obtaining point cloud coordinates of an image data loss area caused by electromagnetic interference, and mapping the point cloud coordinates to an image coordinate system to obtain a three-dimensional geometric complemented image; and constructing a dynamic occupation grid map of the underground environment according to the three-dimensional geometric complementation image and the obstacle position confidence map, and planning an obstacle avoidance path of the mining equipment according to the dynamic occupation grid map. And the obstacle avoidance capability of the mining equipment is improved, and safe and efficient coal mining operation is guaranteed.

The invention relates to an image segmentation method for a burn wound area, and the method comprises the steps: collecting burn wound images under different spectrums through a multispectral imaging device, and enabling the burn wound images to comprise a plurality of wavebands, namely visible light, infrared light and ultraviolet light; for each spectral image, analyzing reflection and absorption characteristics of a burn area under different wavebands by using optical characteristics of biological tissues, and marking a potential burn area; designing a self-adaptive algorithm, and dynamically adjusting parameters, including exposure and light intensity, of imaging equipment based on feedback of different spectral characteristics; the feature contrast of the burn area is enhanced; preliminarily delimiting the boundary of the burn region according to the enhanced spectral image through a threshold segmentation or region growing algorithm; inputting the multispectral image into a hierarchical processing model, and decomposing different tissue levels of the burn area including epidermis and dermis based on different wave band or color information; on each layering result, a feature extraction algorithm including texture analysis and edge detection is used for extracting detail features of each layer.

The invention relates to the technical field of hydrological monitoring, and discloses a river flow monitoring method and system fusing multi-source data, and the method comprises the steps: collecting river flow data and river water level data through employing a high-precision sensor group, and collecting river image data through employing an unmanned plane; filtering the river flow data and the river water level data by adopting a Kalman filtering method to obtain filtered river flow data and filtered river water level data; performing edge detection on the riverway image data by adopting a Canny edge detection method to obtain riverway boundary information; on the basis of the riverway boundary information, a cubic spline interpolation method is adopted for interpolation, a smooth first boundary line and a smooth second boundary line are obtained, and the actual flow of the riverway is calculated on the basis of the filtered riverway water level data and the riverway flow velocity data; judging whether the actual flow of the riverway is greater than a riverway flow preset value, and if yes, sending alarm information; the actual flow of the river channel is calculated by fusing the multi-source data, and the accuracy of monitoring the flow of the river channel is improved.