3198 results about "Optical image" patented technology

The invention relates to the technical field of LED display, solves the problem that the existing LED display defect detection and parameter adjustment technology is lack of multi-modal information fusion and intelligent process control capability and is difficult to meet the quality control requirement of a high-precision display product, and provides an LED display defect prediction and process adjustment method and system based on multi-modal fusion. The method comprises the following steps: performing multi-modal data fusion processing on optical image data, electrical test data and thermal infrared imaging data corresponding to a to-be-tested LED display screen to obtain fused data; inputting the fused data into a pre-trained defect recognition model to obtain a defect recognition result; according to a process parameter adjustment strategy corresponding to the defect identification result, adjusting the original process parameter to obtain a target process parameter; and according to the target process parameters, process flow correction processing is carried out, and a qualified LED display screen is produced. According to the method, the defect identification precision is improved, and the quality control requirement of high-precision LED display screen production is met.

Systems and methods are disclosed for classifying objects using electro-optical and synthetic aperture radar images through multi-modal feature alignment and fusion. A computing system acquires and preprocesses image data, then aligns features across modalities using a multi-modal alignment engine. A cross-modal attention fusion network extracts and integrates complementary information using transformer-based attention mechanisms. A modality-specific feature extraction framework processes EO and SAR images through specialized branches, ensuring optimal feature representation. An adaptive fusion decision system dynamically determines the best fusion strategy based on image quality and confidence scores. A self-supervised consistency controller enforces alignment between EO and SAR features using contrastive learning. The fused representations are processed by a neural network to generate object classifications. This system improves accuracy and robustness in environments where one modality may be degraded or missing, enhancing applications such as remote sensing, surveillance, and autonomous navigation.

The invention provides an unmanned aerial vehicle identification method and system for low-altitude security and protection. An optical compensation parameter set for unmanned aerial vehicle imaging optimization is generated in real time through a joint optimization model of an ambient light intensity change rate and a background motion vector field, and an original optical sequence in a target capture window is processed by using the parameter set. And constructing a time domain deconvolution kernel in combination with the motion characteristics of the unmanned aerial vehicle to generate an enhanced optical image resistant to motion blur. Non-linear weighted fusion is carried out through a disturbance intensity evaluation function, and a confrontation disturbance feature mask is formed. The enhanced optical image and the confrontation disturbance feature mask are subjected to airspace superposition operation, a multi-scale residual network is adopted to carry out target confidence estimation on the superposed image, an unmanned aerial vehicle recognition result is generated, and the unmanned aerial vehicle recognition accuracy and the anti-interference capacity in the low-altitude complex environment are remarkably improved through the technical scheme provided by the invention.

The invention discloses a geological disaster deformation monitoring method based on remote sensing data, and relates to the technical field of environment monitoring, and the method comprises the following steps: S1, data source selection and preprocessing; s2, region feature extraction; s3, differentiating the multi-temporal remote sensing images; s4, analyzing a time sequence; s5, performing three-dimensional reconstruction on the deformation area; s6, settlement and landslide monitoring; s7, performing fusion analysis on environment and meteorological data; s8, performing data anomaly detection and automatic early warning; and S9, risk assessment and influence analysis. According to the geological disaster deformation monitoring method based on the remote sensing data, SAR, optical images and LiDAR data are combined, the defect of dependence of a single data source in the prior art is overcome, dynamic data fusion and time sequence analysis are used, multi-dimensional real-time monitoring of earth surface deformation is achieved, the monitoring precision and timeliness are improved, and the geological disaster deformation monitoring method based on the remote sensing data has good application prospects. More comprehensive geological disaster early warning can be provided, and the effectiveness of disaster prevention and control is greatly enhanced.

The invention provides an underwater target detection method based on multi-modal features and domain adaptation. The method comprises the following steps: S11, acquiring a sonar image, an optical image and environmental data; s12, extracting a sonar feature and an optical feature, encoding the environment data into an environment channel weight, and dynamically adjusting a fusion proportion of the sonar feature and the optical feature through the environment channel weight to obtain a fusion feature; s13, performing spatial attention calculation on the sonar features to obtain a spatial weight map, enhancing the optical features by using the spatial weight map, and performing forced alignment with the sonar features at the target edge; and S14, decoupling the fusion feature into a synthetic domain feature, decoupling the fusion feature and the environment data into a real domain feature, and gradually aligning the synthetic domain feature and the real domain feature through asymptotic domain alignment to complete construction of a target detection model. According to the invention, multi-modal data acquisition, dynamic feature fusion and decoupling and embedded real-time detection are combined, so that the precision of underwater target monitoring is remarkably improved.

The invention relates to the technical field of three-dimensional reconstruction, in particular to a multi-mode remote sensing data driven refined city three-dimensional reconstruction method and system, and the method comprises the steps: obtaining an original satellite optical image for providing city surface information; synthetic aperture radar (SAR) data is obtained and used for assisting reconstruction of a point cloud data missing region; the method comprises the following steps: acquiring laser radar data carried by a flight aerial carrier for acquiring high-precision three-dimensional point cloud; performing data preprocessing; carrying out fusion on the preprocessed multi-modal data; on the basis of the fused features, complementing space detail information; on the basis of the refined features, progressive reconstruction is carried out, vertexes and edges are gradually added from the initial vertexes, and a grid model is constructed; in the reconstruction process, performing semantic segmentation on the grid model by using a multi-category segmentation network, and judging the category of each triangular patch; time sequence reconstruction is completed through fusion of multi-temporal data, and a dynamic city change model is generated; and complementation of advantages of different modal data is realized.

The invention discloses a high-resolution remote sensing image accurate classification system and method based on deep learning multi-modal fusion, and the method comprises the steps: S1, carrying out the preprocessing and fusion optimization of multi-modal data; S1.1, carrying out the standardization and normalization: carrying out the standardization and normalization of remote sensing data of different modals, and eliminating the influence caused by the difference between the different modals, the difference between the resolution, the spectral range and the like; for optical images, contrast may be enhanced by histogram equalization. The method aims at solving the problems of data heterogeneity, calculation efficiency, over-fitting, difficulty in labeling, real-time performance, interpretability and the like in an existing method by adopting multi-modal data optimization preprocessing, deep fusion model design, automatic labeling and semi-supervised learning, a lightweight model and hardware acceleration technology and a strategy for enhancing interpretability. Through the optimization, the system can maintain high classification precision, improve the calculation efficiency, reduce manual intervention, enhance the transparency and generalization ability of the model, and meet the actual application requirements.

The invention provides a railway line monitoring alarm method and system, and relates to the technical field of computers, and the method comprises the steps: continuously collecting a dynamic monitoring data set comprising a track structure vibration waveform sequence, a track surface optical image sequence and an environment meteorological parameter sequence through a plurality of sensor nodes of a target railway line; carrying out multi-modal feature fusion processing on the dynamic monitoring data set, generating a comprehensive state evaluation matrix containing a vibration energy distribution vector and the like, carrying out risk alarm grade classification on the comprehensive state evaluation matrix by utilizing a preset track safety evaluation model, and outputting an abnormal event type code and an abnormal region position code; then, a multi-stage linkage control instruction set containing a train emergency braking triggering instruction and the like is generated based on the multi-stage linkage control instruction set, the multi-stage linkage control instruction set is synchronously transmitted to a central dispatching system, the central dispatching system is driven to execute rail blocking, train speed regulation and control and equipment maintenance operation according to instruction priorities, and comprehensive and intelligent monitoring and efficient alarm response to railway lines are achieved.

The invention provides a visual inspection system and an application method thereof. The system comprises an image acquisition module used for acquiring a multi-angle optical image and laser three-dimensional point cloud data; the data preprocessing module is responsible for denoising, geometric correction and multi-modal data alignment; the feature extraction module extracts texture, edge and defect features through a convolutional neural network; the defect detection module identifies cracks, scratches and foreign matters based on feature fusion; the adaptive optimization module dynamically adjusts a detection threshold value and classifier parameters; the result output module generates a detection report and marks defect positions; the feedback calibration module corrects the weight of the detection model according to an artificial rechecking result; the equipment control module triggers the sorting device to remove defective products; and the performance monitoring module counts the detection accuracy and the system response delay. According to the invention, the detection accuracy and the system stability can be improved.

The invention discloses a multi-modal remote sensing intelligent identification method for hidden geological disasters. The method comprises the following steps: carrying out normalization processing on multi-modal remote sensing data; constructing a pixel-level cross-modal visual converter; constructing a pixel-level cross-modal attention module, wherein the pixel-level cross-modal attention module comprises a pixel self-attention sub-module, a pixel mutual-attention sub-module and a layer adaptation mixing module; a layer adaptive noise mechanism is introduced, and the weight ratio of intra-modal self-attention and inter-modal mutual attention in different levels is adjusted in a self-adaptive mode; constructing a relation discriminator, and modulating the attention calculation process by evaluating the difference between modals of corresponding points in the space; training the model, adopting a multi-task loss function including semantic segmentation loss and boundary perception loss to perform joint optimization, and generating a concealment geological disaster segmentation result. According to the method, complementary information of an RGB optical image, an InSAR deformation rate image and DEM elevation data is fully utilized, and accurate recognition of the hidden geological disaster is realized through an efficient pixel-level cross-modal fusion mechanism.

The invention relates to the technical field of machine tool casting detection, and discloses an automatic detection system for surface defects of machine tool precision castings. The system comprises a surface information acquisition core module, a first defect identification core module, a second defect identification core module and a defect type fusion core module. The surface information acquisition module is used for synchronously acquiring real-time optical images and process parameter data in production aiming at the surface of the casting part, and constructing a defect diagnosis characteristic spectrum and an auxiliary text according to the real-time optical images and the process parameter data; the first defect recognition module inputs the atlas and the auxiliary text into a pre-training double-flow convolutional neural network to generate a first classification result of defect types; a second defect identification module extracts defect mechanism characteristic values from the atlas and matches the defect mechanism characteristic values with a pre-stored defect mechanism knowledge base to obtain a second classification result; and the defect type fusion module fuses the two types of results to determine a target defect type. The system solves the problems of single detection information and identification deviation in the prior art, improves the detection accuracy and real-time performance, and meets the requirements of different production scenes.

The invention provides a multi-modal remote sensing and knowledge graph-based urban planning decision-making method and system, and the method comprises the steps: integrating multi-source heterogeneous data, achieving the feature alignment and fusion of an optical image and SAR data in a satellite remote sensing image through a deep learning technology, and generating an urban ground feature feature vector; associating the urban ground feature feature vector with an urban planning policy database, outputting a structured early warning report of an illegal construction early warning event set and a policy compliance label, and forming a dynamic policy constraint condition for subsequent multi-objective optimization; processing historical traffic flow data based on the dynamic graph model, and outputting a time-space distribution prediction result of future traffic conditions; and generating a Pareto optimal city planning scheme by combining multi-objective optimization with a spatial-temporal distribution prediction result of a future traffic condition. According to the method, high-precision urban surface feature classification, real-time violation extension early warning and traffic flow accurate prediction are realized through multi-modal remote sensing data fusion and a space-time knowledge graph technology, and multi-target optimization and digital twinborn verification are combined, so that the planning efficiency is improved, and extension applications such as carbon neutralization are supported.

The invention discloses a metal 3D printing forming precision monitoring method and device and electronic equipment thereof, and relates to the technical field of metal 3D printing, and the method comprises the following steps: obtaining multi-modal sensor data, collecting multi-dimensional data in the metal 3D printing process based on the multi-modal sensor data, determining an optical sensor to record the molten pool form and powder distribution, and determining the metal 3D printing forming precision. The thermal infrared camera is determined to monitor temperature field distribution, the acoustic sensor is determined to collect and print medium-high frequency sound wave signals, and defect signals are identified; extracting boundary and geometric features from the optical image based on a convolutional neural network according to local preprocessing data of an edge computing device, and extracting a key temperature change area in response to thermal field gradient analysis; spectral features are extracted from the acoustic data based on time-frequency analysis. According to the invention, through synchronous acquisition of optical, thermal and acoustic multi-modal data, data fusion and defect detection are realized by using an advanced deep learning technology.

The invention provides an automatic target identification and tracking method and system for an intelligent pod. The method relates to an automatic target identification and tracking method of an intelligent pod, and comprises the following steps: generating a three-dimensional point cloud through radar array scanning, clustering adjacent points into a point cloud cluster of an independent target, and extracting motion parameters; when the pod moves, the radar beam direction is adjusted according to the target position. And registering the point cloud cluster with the optical image, establishing a cross-modal association identifier for the occlusion target in combination with the geometric features of the point cloud and the visual features of the image, updating the trajectory of the occluded target according to the cross-modal association identifier, and realizing continuous tracking. The invention relates to automatic target identification and tracking of an intelligent pod. Through cross-modal data fusion of radar and vision, continuous recognition and trajectory tracking of the shielded target by the intelligent pod are realized, and the target tracking robustness in a complex environment is improved.

The invention relates to the technical field of topographic mapping, in particular to a DEM super-resolution reconstruction method and system combined with a depth map, and the method comprises the steps: constructing a multi-modal data set which comprises an optical image, a depth map, a low-resolution DEM image and a high-resolution DEM image which are paired; designing a multi-modal fusion super-resolution network; designing a pseudo Siamese feature extraction module to extract low-level features and high-level features of the optical image, the depth map and the low-resolution DEM image; fusing the low-layer feature map generated by the pseudo Siamese feature extraction module in a shallow layer through a multi-scale feature fusion module, and fusing the deep-layer feature map in a deep layer; outputting a high-resolution DEM image through a DEM reconstruction module; and training the multi-modal fusion super-resolution network by using the multi-modal data set and the collaborative loss function. According to the method, the depth map generated by the optical image is used as priori knowledge to guide the DEM super-resolution reconstruction task based on the basic depth large model, a complex fine adjustment process is not needed, and the DEM reconstruction precision and efficiency are improved.

The invention discloses a wind power blade defect data automatic acquisition and analysis method based on an unmanned aerial vehicle, and particularly relates to the technical field of data acquisition and analysis. The method comprises the following steps: collecting blade surface heat distribution data, locally applying a heat source to a blade, collecting a temperature attenuation curve, calculating a heat diffusivity gradient difference value and a temperature attenuation time index, integrating a multi-parameter space, and classifying a heat distribution abnormal region into a normal region and a real defect region in combination with a deep learning model; cross validation is carried out through optical images and ultrasonic data, so that the detection accuracy is improved; dynamically adjusting a sensitivity threshold value and model parameters of the thermal imager according to a classification result, and generating a detection report; through data feedback iterative optimization model and task planning, the problems of misjudgment and missed judgment of the thermal infrared imager in complex material distribution are effectively solved, the accuracy, reliability and automation level of wind power blade defect detection are improved, and important support is provided for efficient wind power operation and maintenance.

The invention discloses a solar silicon wafer visual detection method, and particularly relates to the technical field of silicon wafer detection. The method comprises the following steps: acquiring optical images of different incident angles, synchronously acquiring infrared transmission images, performing normalization processing, edge enhancement filtering and self-adaptive threshold segmentation on the acquired multi-angle images, extracting suspected crack areas, and respectively calculating a gray gradient change rate and a stress abnormal degree, so as to obtain a suspected crack area; calculating a crack credibility score according to the gray gradient change rate and the stress abnormal degree through a weighted scoring method, and comparing the crack credibility score with a preset crack detection threshold value to distinguish real hidden cracks and stress pseudo defects; if the credibility score is lower than the threshold value, secondary sampling is carried out by adjusting the illumination angle, and the judgment result is further optimized; according to the method, the false rejection rate caused by lattice stress pseudo defect misjudgment is effectively reduced, meanwhile, missing detection of real subfissures is avoided, and the accuracy, stability and production yield of silicon wafer defect detection are improved, so that the long-term reliability of a photovoltaic module is enhanced.

The invention discloses an SAR (Synthetic Aperture Radar) small-scale target detection system and method based on deep learning, and aims to solve the problems of low detection precision, inaccurate positioning and poor robustness of a small-scale target under a complex background. The system performs preprocessing through adaptive guided filtering and improved multi-scale local self-adaption to generate a high-quality candidate region; a content perception feature recombination network is combined with a lightweight multi-head attention converter and a dynamic deformable fusion unit, cross-level dynamic aggregation of shallow texture and deep semantics is achieved, and the small target perception ability is enhanced; a modified Bhattacharyya distance loss function is introduced to optimize bounding box matching, and the small target positioning precision is remarkably improved. In addition, the system adopts a multi-stage cascade detection architecture to gradually optimize candidate target screening and regression, and the recall rate is improved through weak target recovery and geometric consistency verification. The multi-source data fusion module combines polarization characteristics and optical image information to enhance the cross-modal detection capability.

The invention relates to the technical field of over-water and under-water target identification, and discloses an over-water and under-water target detection and positioning method based on acousto-optic fusion, and the method comprises the steps: obtaining an optical image of an over-water part of a target and a sonar image of an under-water part of the target, and carrying out the preprocessing of the optical image and the sonar image, obtaining acousto-optic fusion pair data of time synchronization and space alignment; inputting the acousto-optic fusion pair data into a pre-trained target detection model to obtain a target detection result; inertial guidance information is obtained, and on-water position information and underwater position information of the target are determined based on the inertial guidance information; and determining the position information of the target according to the preset credibility of the overwater position information and the underwater position information. According to the method, the overwater optical image information and the underwater sonar information are fused, the target is detected and positioned in combination with inertial guidance information, the target with the overwater part and the underwater part at the same time can be effectively detected and positioned, and the detection accuracy and the positioning precision are improved.

The invention discloses an underwater target detection method based on YOLOv8, and provides a corresponding solution for solving the problems of low contrast ratio, target imaging deformation, difficulty in detection of a small recognized target and the like in underwater optical image target detection so as to improve the underwater target detection precision. Firstly, a module with a receptive field attention mechanism is designed to be used for constructing a trunk feature extraction network, the multi-scale adaptive capacity of the model is improved from two aspects of receptive field range adjustment and feature randomness aggregation, and the robustness of the model to deformation target detection is improved; a smooth dynamic detection head is designed to replace an original detection head, and a multi-scale attention mechanism of the dynamic detection head and the smooth characteristic of a Softplus activation function are introduced, so that the characteristic response is more smoothly enhanced, and the detection performance of a fuzzy target is improved; finally, a WIS-IoU loss function is designed, quality evaluation is conducted on the anchor frame through a dynamic non-monotonic focusing mechanism of Wise-IoU, Shape-IoU considers shape and scale information of a target frame, then the concept of an Inner-IoU auxiliary bounding box is introduced, positioning precision and shape consistency are balanced, the model is evaluated more accurately, and training and optimization of the model are guided. The target detection network is more suitable for target detection in an underwater complex environment, and the underwater image target detection precision can be improved.

The invention discloses a submarine cable pipeline detection method based on underwater acousto-optic fusion, and relates to the related field of underwater detection.The method comprises the steps that underwater sonar image data and underwater optical image data are acquired through underwater sensor equipment; carrying out preprocessing extraction to obtain sonar rectangular image data and standard optical image data; performing feature extraction by using a parallel double-branch convolution-Transform network to obtain an acoustic mode embedded feature set and an optical mode embedded feature set; carrying out attention perception and weighted fusion to obtain acousto-optic multi-modal fusion level features; decoding is carried out through a multi-mode decoder structure, and acousto-optic fusion pixel-level features are obtained; and carrying out detection classification by using a detection regression head and a classification head to obtain a submarine cable pipeline detection result. The technical problem that existing submarine cable pipeline detection is insufficient in detection precision and robustness is solved, and the technical effects of improving the detection precision and enhancing the detection robustness are achieved.

The invention discloses an air-ground integrated measurement method for an original terrain, and relates to the technical field of engineering surveying, and the method comprises the steps: S1, carrying multi-sensor aerial survey by an unmanned aerial vehicle, obtaining an image and POS positioning data, scanning a blind area of the unmanned aerial vehicle by using a three-dimensional laser scanner, obtaining laser point cloud data, and carrying out the scanning of the laser point cloud data; constructing a multi-source data set including optical images, POS positioning data and laser point cloud, and forming an air-ground complementary data acquisition mode; s2, geometric correction and aerial triangulation encryption are carried out on the unmanned aerial vehicle image, denoising and multi-station registration are carried out on the laser point cloud data, and the data quality is improved; s3, unifying a multi-source data space reference through coordinate system unification, feature fusion and decision-level fusion, and realizing feature complementation; s4, constructing a digital elevation model and a digital surface model based on the fused data, and generating a topographic mapping map; the method breaks through the limitation of a single technology through air-ground technology complementation, and ensures the global data coverage of complex terrains.

The invention provides a multispectral fusion-based valve optical image enhancement identification method and system in severe weather, and the method comprises the steps: obtaining multispectral optical image data and mechanical dynamic response characteristics of an industrial pipeline valve in severe weather; based on the mechanical dynamic response characteristics, separating the multispectral optical image data to extract multiband texture characteristics associated with the reflection characteristics of the valve material; double amplitude correction of a spatial domain and a frequency domain is carried out on the multiband texture features; distributing a spectrum fusion weight for the multispectral optical image data according to the corrected multiband texture features; and generating an enhanced optical image matched with the actual mechanical state of the valve based on the spectrum fusion weight and the corrected multiband texture features. According to the method, the recognition accuracy of defects such as valve surface cracks and deformation in severe weather is improved.

The invention relates to the technical field of landslide information extraction, in particular to an SAR image landslide information extraction method based on a deep learning algorithm. The method comprises the following steps: acquiring an SAR image and an optical image of a seismic region; mapping the landslide boundary marked in the optical image to the SAR image so as to mark the SAR image to obtain a label image; constructing a data set based on the original image and the tag image of the SAR image; training a pre-constructed semantic segmentation model by using the data set; and generating a segmentation image based on the trained semantic segmentation model to extract a landslide region. On the basis of the SAR image with rich polarization characteristics, the deep learning semantic segmentation network is used for landslide region segmentation, and the better segmentation precision is realized.

The invention discloses a mobile phone silica gel shell appearance detection system based on an optical image sensor, and relates to the technical field of appearance defect detection and intelligent visual identification. An image acquisition module acquires a high-quality multi-channel image through multi-angle synchronous acquisition; the dynamic light field regulation and control module realizes self-adaptive regulation of a regional light source based on reflectivity perception and a gray gradient change rate; the image processing and defect identification module fuses edge features, texture changes and a deep learning model to realize pixel-level defect classification; the material identification and shielding module identifies a non-silica gel area through multi-dimensional spectral features and eliminates interference; a time sequence defect evolution analysis module models a defect evolution track; the trend prediction module is used for predicting the extension trend of potential cracks and fatigue areas; the control feedback module realizes intelligent judgment and response linked with the manufacturing system; the system can realize high-precision and high-robustness automatic defect detection and risk prediction, and is suitable for online quality monitoring of large-batch flexible products.

The invention discloses a multi-source remote sensing image zero sample change detection method, and relates to the technical field of remote sensing image processing, and the method comprises the following steps: obtaining remote sensing images collected by two or more remote sensing sensors at different time points in the same geographic area, the image types including optical images and radar images; preprocessing each source image, unifying the spatial resolution and the registration precision, and denoising and standardizing the image; according to the method, the cross-modal shared semantic embedding space is constructed and unsupervised comparative learning is introduced, so that the semantic consistency of the multi-source remote sensing image is effectively improved, and the change recognition capability of the model under the zero sample condition is enhanced; and meanwhile, a difference fusion calculation and structure consistency constraint module is adopted, so that the boundary judgment precision of a change region and the overall structure consistency are improved, and the accuracy and stability of a detection result are remarkably improved.

The invention relates to the technical field of land resource monitoring, in particular to a land resource dynamic monitoring and early warning method and system based on multi-source remote sensing data fusion, and the method comprises the steps: employing an unmanned plane to periodically collect optical images, SAR echoes and LiDAR point clouds, constructing a ground three-dimensional digital model, and carrying out the land parcel division; performing fusion to form a multi-dimensional feature vector, establishing an LSTM land parcel feature evolution model, and predicting a change rate interval of each feature in a current period based on a historical sequence; constructing a time sequence difference change detection algorithm, calculating a land parcel change rate, and screening potential abnormal land parcels by taking a prediction interval as an anomaly judgment threshold value; a double-branch convolutional neural network is adopted to identify crop states, growth stages and construction violation behaviors, abnormity is judged and determined, and confidence is given; spatial clustering is carried out on determined abnormal land parcels, accurate boundaries are obtained in combination with a three-dimensional model, multi-level early warning information is generated, and the decision-making efficiency and response speed of land resource monitoring are improved.

The invention provides a building damage detection method and device for coupling multiple features of an SAR image and an optical image. The method comprises the following steps: acquiring an SAR image, an optical image and an unmanned aerial vehicle image corresponding to a research area in a pre-disaster scene and a post-disaster scene respectively; constructing a research area change detection image set based on the SAR image and the optical image, and constructing a vector data set corresponding to the research area based on the optical image and the unmanned aerial vehicle image; according to the vector data set, extracting multi-scale features corresponding to the building vector units from the change detection image set to construct a multi-scale feature library; and training the damaged building classification network by using the multi-scale feature library, wherein the trained damaged building classification network is used for carrying out damage detection on the research area. According to the method, the problems of single data source, relatively high feature extraction limitation, weak model generalization ability, insufficient real-time performance and robustness and the like in the prior art can be effectively improved.

The invention relates to an underwater image processing technology in the field of underwater autonomous perception, in particular to an underwater low-quality image enhancement method based on a Laplacian pyramid and contrast learning. Comprising the following steps: S1, image input and multi-scale decomposition: decomposing an input image into a low-frequency residual layer and a plurality of high-frequency detail layers through Laplacian pyramid decomposition; s2, inputting the low-frequency residual error layer into a global illumination and color correction sub-module to obtain an enhanced low-frequency residual error layer; s3, inputting the enhanced low-frequency residual error layer and the multi-scale high-frequency detail layer into a frequency domain enhancement feature module to obtain multi-scale enhanced high-frequency detail layer output; s4, performing progressive reconstruction on the enhanced low-frequency layer output and the high-frequency enhancement layer output of each scale according to the inverse process of the Laplacian pyramid; and S5, outputting a result and applying. The method can be used for sharpening optical image data in the operation task of the autonomous underwater vehicle, and the image quality is improved.

The invention provides a plastic cellular board hot-pressing defect identification method and system, and relates to the technical field of defect identification. The method comprises the following steps: monitoring the position of a plate and generating position information; applying instantaneous local thermal excitation to the surface of the plate to generate a thermal excitation area; optical image data and infrared thermal image data are collected; obtaining temperature difference data according to the infrared thermal image data; distortion correction and noise filtering are carried out on the optical image data to obtain corrected optical image data; registering the temperature difference data with the corrected optical image data to obtain registered multi-modal data; and the defect problem is judged by analyzing the registered multi-modal data. The method provided by the invention overcomes the problem that the defect optical and infrared thermal imaging characteristics are difficult to recognize along with time attenuation when the plate is in a rapid moving and continuous cooling environment, and realizes robustness recognition and accurate positioning of hot pressing defects below millimeter level.