2629 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 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 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 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 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 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 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.

The invention provides a method for converting an SAR image into an optical image based on a multi-mode conditional diffusion model. The method comprises the following steps: firstly, collecting SAR image and optical image data with a pairing relationship, and ensuring that a sample has diversity in the aspects of scene types, target structures and image styles; secondly, an image-language multi-modal model is adopted to generate text descriptions of SAR and optical images respectively, unified language descriptions are constructed through semantic analysis and fusion, and a light-SAR-text three-modal training sample set containing structure, semantic and style information is formed; according to the method, a denoising diffusion model guided by a multi-modal condition is designed and trained, a noise adding-denoising reconstruction process of an original optical image is taken as an optimization target, an SAR image, language description and a style image are introduced as multi-modal prompt conditions, and the expression of the generated image in the aspects of structure reduction, semantic alignment and style presentation is comprehensively guided.

The invention relates to the technical field of power transmission line monitoring, in particular to a multi-modal data fusion power transmission line digital twinning real-time monitoring system and method, and the system comprises a data collection module which is responsible for collecting optical images, environment characteristic data, current waveform data and point cloud data, and transmitting the data to a digital twinning module; the digital twinborn module constructs a three-dimensional digital twinborn body of the power transmission line, fuses the multi-modal data through a graph neural network and an attention mechanism, generates multi-modal fusion features, establishes a dynamic correlation model of conductor temperature-sag-current-carrying capacity based on the features, and transmits the dynamic correlation model to the online monitoring module; the on-line monitoring module fuses real-time collected data and historical data, analyzes the line state, generates multi-level early warning information and outputs a line state analysis result and fault prediction information, the system adopts an innovative data fusion method, the sensitivity and accuracy of fault detection are improved, and the operation safety and efficiency of the power transmission line are improved.

The invention discloses an acoustic-optical imaging semantic fusion detection method for underwater defects of a hydraulic structure, and the method comprises the steps: obtaining underwater acoustic and optical images of the hydraulic structure at the same time through underwater acoustic-optical imaging sensing for the underwater defects of the hydraulic structure; and on the basis, acoustic semantic features in the acoustic image are extracted through a semantic feature extraction module, optical image features in the optical image are processed by taking the acoustic semantic features as guidance, and a defect detection result is obtained when an underwater defect target exists in the image, so that underwater defect sound-light imaging semantic fusion detection is realized. The technology is suitable for health monitoring of various dam system hydraulic structures, and operation safety and water safety of hydraulic engineering are guaranteed.

The invention provides an underwater target identification method and system based on multi-source sensor data fusion. The method comprises the following steps: firstly, synchronously acquiring an original sound wave reflection signal and original optical image data of a target underwater area, then carrying out acoustic compensation processing on the original sound wave reflection signal to generate a target sound wave reflection signal, and meanwhile, carrying out optical compensation processing on the original optical image data to generate target optical image data; performing dynamic weighted fusion on the reliability measurement of the target sound wave reflection signal and the reliability measurement of the target optical image data through an adaptive fusion module to generate an enhanced multi-modal data set; and finally identifying the specific category of the underwater target through a multi-modal feature matching and decision-making mechanism. According to the technical scheme provided by the invention, the limitation of a single sensing mode is overcome, the quality of collected information is improved, the direct confirmation efficiency of common target recognition is also improved, and high accuracy and data reliability of target category recognition in a complex scene are ensured.

The invention belongs to the field of three-dimensional surveying and mapping, and particularly relates to a multi-precision three-dimensional surveying and mapping data fusion method based on dynamic modeling, which comprises the following steps of: assigning low-level semantic tags to LiDAR point cloud geometric features, and assigning high-level semantic tags to optical image texture features; defining a semantic tree structure, and establishing a cross-scale semantic association initial anchor point; constructing a cross-modal graph structure, projecting LiDAR point cloud nodes to optical image neighborhood superpixel nodes, and connecting and aggregating multi-scale semantic features; inputting geometric and image residual texture features by using a U-Net generator and outputting virtual textures; geometry and texture feature fusion and semantic and geometry collaborative optimization are realized through gating weighted feature fusion. According to the method, the problem of inconsistent multi-precision data semantic expression is systematically solved, semantic consistency is improved, texture deficiency is filled, and dynamic balance between geometric fidelity and semantic enrichment is realized.

The invention provides a dynamic weight adjustment pest monitoring method and system based on a multi-modal remote sensing large model, and the method comprises the steps: obtaining data of a to-be-monitored region from a multi-source remote sensing platform, including a high-resolution optical image, a multispectral image and an SAR image, and carrying out the image preprocessing; extracting a multi-modal feature by using a feature extraction network, and constructing an FPN network structure of each modal for feature fusion to obtain a multi-modal fusion feature map; then multi-modal feature alignment is carried out, and multi-modal feature fusion is finally completed through a double attention module; feature enhancement is carried out by utilizing dynamic multi-granularity contrast learning, and features are extracted at different levels respectively; and through multi-granularity contrast learning, pixel-level, object-level and image-level contrast loss is calculated, and total loss is obtained through weighted summation and is used for model training. And deploying the trained model to an unmanned aerial vehicle or a satellite system, and collecting and processing remote sensing data in real time.

The invention discloses a nondestructive testing device for a steel profile and a testing method of the nondestructive testing device. According to the method disclosed by the invention, the ultrasonic technology is combined with optical imaging, so that synchronous detection of surface and internal defects can be completed at one time. Ultrasonic waves penetrate through the interior of the steel profile, and hidden defects such as cracks and air holes are accurately recognized; the optical camera module captures visible problems such as surface scratches and corrosion in real time. The integrated detection mode avoids the tedious process of step-by-step operation required by a traditional method, for example, magnetic powder detection of surface defects and ultrasonic scanning of an internal structure are not needed, the detection period is greatly shortened, and the method is especially suitable for steel structure buildings, bridge engineering and other scenes with high requirements for detection efficiency and has good application prospects. The full-process automatic design effectively reduces the influence of human intervention, and ensures the scanning continuity of the detection area; the intelligent algorithm automatically analyzes the ultrasonic echo signal and the optical image data, and judges the defect level according to the preset standard, thereby avoiding subjective misjudgment of manual visual inspection.

The invention provides wafer detection equipment and a wafer detection method, which are used for solving the problem of how to improve the wafer detection efficiency. The wafer detection equipment comprises a double-sided detection unit, the double-sided detection unit comprises a scanning carrying platform, a front optical imaging module and a back optical imaging module, and the scanning carrying platform can linearly move along a first direction; the front optical imaging module and the back optical imaging module are respectively arranged on two opposite sides of the scanning stage and are respectively provided with a back bright field light source and a back dark field light source; when the scanning stage moves along the positive direction of the first direction, the front bright field light source and the back bright field light source are synchronously turned on, and the front optical imaging module and the back optical imaging module respectively generate bright field images of the front and back of the wafer; and when the scanning stage moves along the negative direction of the first direction, the front and back dark field light sources are synchronously turned on, and the front optical imaging module and the back optical imaging module respectively generate dark field images of the front and back of the wafer.