146540results about "Image enhancement" patented technology

A system and method are disclosed for generating hyperspectral images from multi-modal sensor data including RGB, LiDAR, thermal, and near-infrared inputs. Training data includes hyperspectral images and corresponding multi-modal measurements. Spectral band grouping is performed based on correlation coefficients. A multi-modal decomposition network with cross-modal attention mechanisms generate reconstructed hyperspectral images by fusing complementary sensor information. A fine-tuning network creates reconstructed RGB images. A comprehensive quality assurance system analyzes spectral consistency, cross-modal coherence, and fusion artifacts to generate quality metrics. Missing data compensation strategies handle corrupted sensor inputs using information from other modalities. The system includes temporal integration for video sequences and multi-resolution processing for different sensor resolutions. Quality metrics guide network weight adjustments to improve reconstruction accuracy while maintaining robustness to sensor failures and environmental variations.

Provided is a road crack detection method, a medium and a product. The lightweight YOLO-MCS road crack detection model is constructed based on an improved yolov8 network. A method of improving the yolov8 network includes: replacing a feature extraction network backbone with a lightweight convolutional neural network MobileNetV3, and embedding a Coordinate Attention Mechanism (CA) module in the lightweight convolutional neural network MobileNetV3; adding a small target detection layer and a Squeeze and Excitation (SE) module at a neck end; and introducing a loss function of power Intersection over Union (IoU) into a head prediction structure. The road crack detection method overcomes the problem that the existing detection algorithm has low detection precision for small cracks and is difficult to be applied to edge devices with limited computing resources.

A system for detecting sensor position change (DCPC) on movable vehicle components includes two or more sensors on at least one of the movable vehicle components. The sensors detect optical information within a distinct field of view (FOV) about an environment surrounding the vehicle. The distinct FOV of each sensor at least partially overlaps with an FOV of at least one other sensor. The system includes controllers that execute an application for DCPC. The DCPC application acquires overlapping optical information from the sensors, computes a conditional correspondence probability distribution for feature points in the overlapping optical information, computes a normalized joint entropy of the feature points, determines that the sensor is in a position manageable by the DCPC, continuously monitors positions of each of the sensors, selectively dynamically aligns the sensors, and ensures that the sensors are calibrated for vehicle perception tasks including advanced driver assistance system (ADAS) functions.

Disclosed in the present invention is a salient contour matching-based method for target measurement in a severe imaging environment. The method specifically comprises: (1) acquiring a binocular image of a target; (2) establishing a global-local joint constraint-based background light estimation model, and removing a scattering effect of a medium in an imaging environment to obtain a restored left eye image and a restored right eye image; (3) learning an original image, and on the basis of a residual between a network reconstructed image and the original image, obtaining target localization prediction maps of the left eye image and the right eye image; and (4) respectively extracting contour lines of the target in the left eye image and the right eye image, constructing feature matching descriptors of contour points, performing stereo matching on the two sets of contour lines by minimizing matching cost, and performing three-dimensional reconstruction on the contour lines in light of calibrated intrinsic and extrinsic parameters to complete the measurement of a key size. According to the present invention, the key sizes of different targets in a severe environment can be accurately measured, thereby providing an effective solution for the problem of measuring the sizes of targets in a severe environment.

The invention relates to a method and a device for detecting machine vision dynamic defects of a precise structural part. The method comprises the following steps: scanning a sample precise structural member to obtain an original image sequence; inputting the original image sequence into a GPU for parallel processing to obtain a preprocessed image sequence and a target feature point set; performing image matching to obtain a camera pose transformation matrix; dynamically correcting the preprocessed image sequence to obtain high-precision three-dimensional point cloud data; inputting the high-precision three-dimensional point cloud data into an improved U-Net deep learning network containing a geometric perception module for training and transfer learning to obtain a curved surface adaptive defect detection model; and based on the curved surface adaptive defect detection model, carrying out full-matrix sampling analysis and geometric model compensation on target three-dimensional point cloud data scanned by the precision structural member to be detected in real time to obtain a target defect detection result. According to the method, the accuracy and robustness of defect identification of the precision structural part are improved.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor that the system validates above a mathematical threshold.

Since defect areas in collected hydro turbine defect images account for a small proportion and there is a significant color difference between surrounding background and defects, which creates too much redundant information and greatly affects detection speed, a method for detecting defects in a hydro turbine top cover based on an improved YOLOv8 model involves first cropping collected defect images to obtain cropped images, then expanding the cropped images by image enhancement techniques such as image flipping and image blurring to obtain a training dataset. A defect detection network based on YOLOv8-CBAM is constructed and trained to generate a defect detection model for detecting the defects in the hydro turbine top cover. The method achieves high precision in defect detection for the hydro turbine top cover.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The invention discloses a visible light and infrared image fusion method based on cross-modal dynamic collaboration. The method comprises the following steps: respectively extracting texture detail features of a visible light image and thermal radiation features of an infrared image through a visible light encoder and an infrared encoder; spatial alignment and channel complementarity optimization of cross-modal features are realized by using a heterogeneous attention collaboration module; and performing layered fusion on deep semantics and shallow detail features through a dynamic gating multi-scale decoder to generate a high-resolution fusion image. According to the method, the problems of feature dislocation, detail loss and unreasonable fusion weight distribution caused by modal difference in the prior art are solved, the detail fidelity, the thermal target saliency and the complex scene adaptability of the fusion image can be remarkably improved, and a high-robustness fusion result is provided for low-illumination environment perception and multi-modal target recognition.

The invention discloses a high-precision image processing method and system based on illumination adaptive compensation, and relates to the technical field of computer vision and image processing, and the method comprises the steps: inputting an original image, and dividing the image into a high-frequency edge layer, an intermediate-frequency texture layer and a low-frequency illumination layer through a multi-scale residual network; acquiring illumination intensity, color temperature and scene categories in real time by using an ambient light sensor and a scene semantic segmentation model, and generating dynamic compensation parameters; carrying out dynamic range expansion on a low-frequency illumination layer based on a physical illumination model, and adjusting the weight of highlight suppression and dark area enhancement through a self-adaptive S-shaped exposure curve; a double-branch generative adversarial network is adopted, noise suppression and super-resolution reconstruction are carried out on the high-frequency layer, and texture detail enhancement is carried out on the intermediate-frequency layer; aligning the data of the depth camera and the infrared sensor with the visible light image through a cross-modal fusion module; and performing tone mapping on the fused image based on human visual characteristics, and outputting an enhanced image with a high dynamic range and reserved details.

The present application relates to a weld seam surface defect detection method and system. The weld seam surface defect detection method comprises: performing multi-angle and multi-surface acquisition of target surface images; then, reconstructing normal maps of the target surface images by means of a high-performance algorithm, and clarifying normal features of defects; and finally, on the basis of the surface normal features, aiming at transverse texture defects and height mutation defects, respectively designing a transverse texture defect detection algorithm based on two stages and rotated rectangle extraction and a height mutation defect detection algorithm based on adaptive dual-threshold segmentation. The present application overcomes texture noise and pseudo-defect interference of ground surfaces, effectively solves the problems of difficulty in feature extraction and positioning of ground surfaces of steel rail weld seams, and has the system operating efficiency superior to that of existing detection methods, thus satisfying requirements of actual production.

The invention discloses a building facade defect intelligent detection method. Comprising the steps of multi-illumination condition adaptive acquisition and preprocessing, multi-view image geometric correction and registration, multi-scale invariant feature extraction and characterization, defect candidate region segmentation under a complex background, defect topological structure reconstruction based on physical constraints, defect parameter accurate quantization and classification, result visualization, report generation and the like. According to the invention, through the technologies of regional adaptive illumination compensation, view angle invariant feature transformation, class defect interference suppression, fracture connection based on physical constraints, sub-pixel-level width measurement and the like, the sub-pixel-level width is measured; the technical problems of defect feature enhancement, complex background interference suppression, multi-view defect unified characterization, defect topological structure reconstruction, accurate parameter quantification and the like under the multi-illumination condition are solved, and the accuracy and the automation degree of building facade defect detection are improved.

Disclosed are a high-performance loosely-coupled multi-modal data fusion system for a smart driving environmental perception system and a vehicle-mounted device, comprising: a fusion detection model based on a modality-independent feature interaction strategy, which is configured for converting a LiDAR point cloud, a camera image, and a millimeter-wave radar point cloud into a unified bird's-eye view representation, and performing multi-modal fusion; and a fusion tracking model based on a motion-appearance feature cascaded coupling data association strategy, which is configured for performing subsequent trajectory tracking and matching according to multi-modal fusion feature information. A VoD data set and a K-Radar data set are selected for training, verifying, and testing the comprehensive performance of the models, and a TensorRT accelerated inference model is applied, then quantized, and deployed to a vehicle-mounted computational testing platform. The present invention is compatible with mainstream sensor deployment solutions, and achieves the efficient complementary fusion of multi-source heterogeneous sensor information, significantly improving the reliability, accuracy, and adaptability of vehicle-mounted perception systems, thereby effectively responding to extreme operating conditions such as complex traffic scenarios and inclement weather.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

The present invention provides a facial recognition system using deep learning methodologies while integrating privacy-preserving capabilities. This system employs convolutional neural networks (CNNs) to extract and classify facial features, ensuring high accuracy in recognition tasks. Moreover, the system addresses privacy concerns by incorporating techniques such as facial feature encryption and anonymization, thereby enhancing user privacy and data security. This invention is applicable across various domains, including security, surveillance, access control, and personalized services, where facial recognition is utilized while preserving individual privacy.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

Systems and methods of sensor data fusion including sensor data capture, curation, linking, fusion, inference, and validation. The systems and methods described herein reduce computational demand and processing time by curating data and calculating conditional entropy. The system is operable to fuse data from a plurality of sensor types. A computer processor optionally stores fused sensor data that the system validates above a mathematical threshold.

According to the PCB production line defect detection system and method based on image processing, a plurality of high-resolution cameras, an infrared thermal imaging camera and a 3D laser scanner are adopted, a dynamically-controlled annular LED light source, polarized light illumination and a structured light projection technology are combined, a foreground and a background are separated through a self-adaptive threshold Otsu method, and the defect detection accuracy is improved. A convolutional neural network denoising model and a frequency domain filtering technology are introduced, welding spot and line features are extracted from a preprocessed image, a coding scheme is designed, multi-modal features are fused, spatial-temporal feature analysis is introduced, fine classification and positioning are performed in combination with SVM preliminary screening and a YOLOv5 deep learning model, an online learning mechanism is introduced, model parameters are dynamically optimized, and a multi-modal image is obtained. An intelligent early warning mechanism is established, automatic grading and alarm sending are carried out, a statistical report is automatically generated, production process parameters are recorded, root cause analysis is carried out in combination with big data analysis, and potential defects are early warned in advance.

The present invention provides an automatic intelligent inspection system for the equipment, facilities and surrounding environment along a railway line, including an unmanned aerial vehicle (UAV), a movable ground base station, a remote server and a working computer, wherein the working computer includes an image processing module that is configured to detect an obtained image by using a deep learning network model trained with a database in advance, obtain any object that is suspected to involve a defect by analysis, and transmit the object for manual review while calculating for the specific geographic coordinate information of the defect, report for early warning after the defect is confirmed by the manual review, inform the operation and maintenance personnel, and store and record the defect. The present invention further provides a method for inspection by using the automatic intelligent inspection system for the equipment, facilities and surrounding environment along a railway.

An exemplary image segmentation method and system that employs, in a deep neural network, (i) an attention gate that fuses features with skip connections and (ii) a graph or multi-scale convolutional attention module that enhances the long-range and local context. The segmented region or image data derived in part from the segmented region can be subsequently employed for diagnosis, controls, planning, assessment, or analysis of various diseases. The image data can be medical images (e.g., from medical instruments), sensor images or videos (e.g., from smart phones, cameras, satellites, etc.), as well as 3D objects or volume.

The invention relates to a multi-robot collaborative navigation method and system based on a visual language large model, and the method comprises the steps: carrying out the analysis according to a task instruction inputted by a user, a semantic map constructed by each robot, and a large language model, and then distributing a subtask instruction to the robot; each robot acquires an environment image in real time, analyzes and predicts the next action by using the visual language navigation large model according to the distributed subtask instruction, and executes the corresponding action and updates the state; an operator monitors the state of the robot and checks the task progress in real time through the man-machine interaction interface, and adjusts the task or deals with abnormity in the execution process when needed, and the robot dynamically adjusts an execution strategy according to feedback information, optimizes the task and completes navigation. Compared with the prior art, the multi-modal data is deeply fused, the robot is endowed with higher environment perception and task understanding ability, the cooperation efficiency of the multi-robot system is improved, and the dynamic environment adaptability is enhanced.

The invention relates to a high-voltage equipment defect detection method based on deep learning and multispectral image fusion, and relates to the technical field of electric power high-voltage equipment state detection. The method comprises the following steps: acquiring an ultraviolet image, an infrared image and a visible light image of the surface of the high-voltage equipment; carrying out image pixel feature-based fusion processing on the ultraviolet image, the infrared image and the visible light image through an image fusion method; establishing a high-voltage equipment defect detection model, and training the high-voltage equipment defect detection model by using the fused image data to obtain a high-voltage equipment defect identification model based on the YOLO-STrans multispectral fusion network; and inputting the ultraviolet image, the infrared image and the visible light image of the outer surface of the power high-voltage equipment into a high-voltage equipment defect identification model to obtain a fault identification result of the to-be-detected power high-voltage equipment. The method can improve the recognition precision of the extremely early insulation degradation and temperature anomaly defects of the surface of the high-voltage power equipment.

The present invention relates to an adaptive sensing-based lightweight monitoring method for a fine crack in a complex background region. The method comprises the following steps: step S1, on the basis of region division, performing automatic acquisition of crack information, wherein PTZ camera sensors are used to automatically perform block-wise acquisition on crack regions; step S2, performing an adaptive complex scale calibration process, using a multi-scale template matching algorithm to adaptively correct distortion information of all regions, and performing real-scale conversion from pixel precision; step S3, constructing a lightweight crack segmentation network to process data processed in step S2; and step S4, by means of a quantitative crack-tracking algorithm based on Euclidean distance similarity classification, performing real-time monitoring on each piece of crack dynamic information. Compared with the prior art, the present invention has advantages such as achieving efficient, accurate, and online monitoring and analysis of cracks.

The invention discloses a building digital twinning three-dimensional reconstruction method and system based on a large model, and relates to the technical field of building digital twinning and three-dimensional modeling fusion, and the method comprises the steps: laying multi-source equipment in a building, collecting building multi-angle data, and carrying out type distinguishing and structured preprocessing. An adaptive deep network model is constructed and trained to identify building features, and the robustness of the model is improved by adopting a multi-type enhancement strategy. Point cloud splicing and coordinate unification are realized by using a fusion algorithm, and twin model updating and synchronization are realized based on a multi-dimensional threshold. According to the method, high-quality input is established for a depth model; building structure feature recognition has the advantages of robustness and precision; and the twinborn model has dynamic updatable capability. The three parts form a complete closed loop, the technical bottlenecks of a traditional reconstruction method in precision, stability and renewability are finally broken through, and a new building digital twinning three-dimensional modeling path facing a complex scene is achieved.

The present invention relates to a railway anomaly detection method and system based on multi-modal data fusion. The method comprises: separately encoding acquired modal data in a railway environment, and concatenating encoded modal data features, wherein the modal data comprises a one-dimensional vibration signal, two-dimensional image data, and 3D point cloud information; performing automatic weight classification on the concatenated multi-modal data features on the basis of an attention mechanism to obtain a weighted feature vector fused with multi-modal information; and adding a location code to the feature vector and using same as an input of an SAM encoder to obtain a segmentation result, and determining an anomaly condition of a railway on the basis of the segmentation result. The present invention achieves high monitoring accuracy, a real-time response capability, and specificity.

The invention relates to the field of semiconductor packaging material defect detection, in particular to a semiconductor packaging material defect detection method based on deep learning, which comprises the following steps: acquiring a surface image, and extracting a two-dimensional contour and a feature point set; preprocessing the image, and separating a packaging material main body area; constructing a two-dimensional defect identification model based on Transform, and outputting a two-dimensional detection result; scanning suspected and unknown defect areas to obtain three-dimensional point cloud data, and extracting geometric and texture features; fusing two-dimensional and three-dimensional data through a space-time alignment model; utilizing the multi-modal fusion model to output defect positions and types; and evaluating the defect importance based on the material node connectivity and the stress distribution, and generating a visual detection report. According to the invention, high-precision detection of semiconductor packaging material defects is realized, the defect identification rate, the positioning precision and the detection efficiency are improved through multi-modal data fusion and a deep learning model, and a visual report can be generated based on material structure quantification defect importance.

The invention discloses a stainless steel surface defect detection method based on machine vision, and relates to the field of material defect detection.The stainless steel surface defect detection method comprises the steps that stainless steel surface images are collected, the collected multispectral surface images are subjected to image fusion, and a multispectral composite image is generated; performing detail enhancement on the low-resolution image through a super-resolution reconstruction algorithm to obtain a high-resolution image set; positioning a defect area position from the high-resolution image set through a defect detection algorithm, and obtaining a defect position data set; extracting a historical image defect feature set and a real-time image defect feature set from the defect position data set, and constructing a defect classification model according to the historical image defect feature set; inputting the real-time image defect feature set into a defect classification model, and performing defect classification and evaluation to obtain a defect classification data set; and generating and distributing a defect detection report. By combining the multispectral imaging algorithm, the super-resolution reconstruction algorithm and the defect classification model, the automation, precision and efficiency of stainless steel surface defect detection are improved.

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.

The present application discloses a face image restoration method based on a state space model, which includes inputting the to-be-restored face image to a restoration model to obtain a restored face image; the restoration model includes an encoder and a decoder; the encoder sequentially includes a first image fusion module, a first multi-scale state space module, a second image fusion module, a second multi-scale state space module, a third image fusion module, and a third multi-scale state space module; the decoder sequentially includes a fourth multi-scale state space module, a first multi-scale attention fusion module, a fifth multi-scale state space module, a second multi-scale attention fusion module, a sixth multi-scale state space module, and a third multi-scale attention fusion module. This method ensures the consistency of face semantic information while restoring the detailed texture, especially low quality face images in real degraded scenes can achieve better restoration results.