273046results about "Image analysis" 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.

The present disclosure provides an underwater detection method for contact leakage of a tunnel joint of a dam culvert, including: obtaining an underwater image sequence of the culvert by an underwater robot; preprocessing and registering the underwater image sequence; extracting particle information appearing in the underwater image sequence based on the registered underwater image sequence; constructing a three-dimensional fluid velocity distribution map based on the particle information, and determining leakage situation according to the three-dimensional fluid velocity distribution map; and superimposing the three-dimensional fluid velocity distribution map on a preconfigured three-dimensional culvert model, and rendering and displaying it. The present disclosure can adapt to the complex and changeable underwater environment inside the culvert, it has a high sensitivity to fine leakage in the deep layer, it can realize the accurate measurement and trend prediction of leakage, and it can automate the detection process to reduce manual dependence.

Provided is a medical image segmentation method based on multi-scale feature fusion. The method includes acquiring medical image data of a same type, and sketching a region of interest (ROI); preprocessing sketched image data; constructing a multi-scale feature extraction module, a multi-scale feature fusion module, and an encoder-decoder deep learning network model; performing 5-fold cross-validation on the network model; and evaluating a medical image segmentation result output by the model with an evaluation index including a dice, an accuracy, a precision and a recall. This application extracts multi-scale features comprehensively from an encoder, a decoder, and connection between the encoder and the decoder in the network, and effectively learns multi-scale information in the image. The multi-scale features are fused by an interactive module, so this application relieves feature conformity caused by direct multi-scale fusion, thereby improving performance of the medical image segmentation.

Systems and methods are provided for machine learning based monitoring. Image data from a camera is received. On the hardware accelerator, a person detection model based on the image data is invoked. The person detection model outputs first classification result. Based on the first classification result, a person is detected. Second image data is received from the camera. In response to detecting the person, a fall detection model is invoked on the hardware accelerator based on the second image data. The fall detection model outputs a second classification result. A potential fall based on the second classification result is detected. An alert is provided in response to detecting the potential fall.

A 3D target detection method based on multimodal fusion and a depth attention mechanism includes the following steps: obtaining and preprocessing original point cloud data and original image data; inputting the preprocessed point cloud data and image data into a 3D target detection network based on multimodal fusion and the depth attention mechanism, where the 3D target detection network based on multimodal fusion and the depth attention mechanism includes a generation phase for 3D bounding box proposals and a refinement phase for 3D bounding boxes, and the network outputs parameters and classification confidence of a target bounding box; training the 3D target detection network based on multimodal fusion and the depth attention mechanism; and processing the collected lidar point cloud data and image data by using the trained detection network, and outputting 3D target information, to realize 3D target detection.

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.

The invention provides a battery film production automation control method and system based on visual inspection, and relates to the technical field of production control, and the method comprises the steps: collecting a film image, carrying out the fusion, generating a fused film image, carrying out the noise reduction, constructing a feature point matching relation, removing wrong point pairs, carrying out the geometric correction, carrying out the sub-pixel-level alignment, and obtaining a registration image; edge enhancement is carried out based on an improved Frangian filtering algorithm, an edge region is repaired and segmented to obtain a film edge contour, three-dimensional reconstruction is carried out, three-dimensional point cloud coordinates are solved, a film three-dimensional contour model is reconstructed, a film defect detection model is constructed, and a film defect distribution diagram and defect confidence are obtained through forward propagation; performing association analysis with a production process, constructing a feature mapping model, generating a defect knowledge base and matching a process parameter optimization strategy, generating a dynamic instruction and sending the dynamic instruction to a station control unit, monitoring operation data, performing closed-loop feedback control, and obtaining a current optimal control scheme.

A small sample steel defect detection method based on an attention feature pyramid mechanism, comprising: first acquiring initial multi-scale feature maps by means of a convolutional neural network, then by means of a top-down and transverse connection mode, using an improved multi-scale attention network to process the initial multi-scale feature maps one by one to obtain intermediate multi-scale feature maps, then by means of a bottom-up and transverse connection mode, using the improved multi-scale attention network to process the intermediate multi-scale feature maps one by one to obtain final multi-scale feature maps, and finally outputting a final classification result; using a pseudo-label semi-supervised learning algorithm to train a detection network model, using a locally linear embedding algorithm to perform dimension reduction and visualization processing on high-level features of training output results, screening for mistakenly labeled and incorrectly labeled images and removing labels, then putting the images into unlabeled image data, and performing re-training to generate pseudo labels so as to expand a training set until the training requirement is met; and inputting an image to be detected into a trained detection network model for identification.

Using a pre-trained and fixed Vision Transformer (ViT) model as an external semantic prior, a generator is trained given only a single structure / appearance image pair as input. Given two input images, a source structure image and a target appearance image, a new image is generated by the generator in which the structure of the source image is preserved, while the visual appearance of the target image is transferred in a semantically aware manner, so that objects in the structure image are “painted” with the visual appearance of semantically related objects in the appearance image. A self-supervised, pre-trained ViT model, such as a DINO-VIT model, is leveraged as an external semantic prior, allowing for training of the generator only on a single input image pair, without any additional information (e.g., segmentation / correspondences), and without adversarial training. The method may generate high quality results in high resolution (e.g., HD).

The present invention discloses an interactive behavior understanding method for posture reconstruction based on skeleton and image features. The steps are as follows: constructing and preprocessing the data set, extracting skeleton and image features, fusing and reconstructing these features, and conducting experimental evaluation and validation. This method retains the purity of skeleton features for human behavior information extraction and uses image features to retain effective environmental information, complementing the model feature information. Skeleton features are extracted using a graph convolution network, enhancing the relevance of input skeleton point information for accurate feature extraction. Effective image features are quickly and accurately extracted through the Vision Transformer network combined with a multi-head attention mechanism.

The invention provides a real scene three-dimensional modeling method and system fusing laser point cloud and images, and belongs to the technical field of urban three-dimensional modeling. According to the method, laser point cloud data and multi-view image data of a target scene are acquired through a synchronous acquisition device, and joint calibration is performed on a laser radar and a camera to generate space-time synchronization parameters. And a dynamic weight registration algorithm is utilized to dynamically adjust the registration weight according to the point cloud density distribution and the image texture complexity so as to realize the multi-level registration of the laser point cloud and the image data. The registered data is used for constructing a three-dimensional geometric model and generating a seamless texture map, meanwhile, semantic-driven hole repairing is carried out on a model missing area, and finally a complete three-dimensional model is output. According to the real-scene three-dimensional modeling method and system fusing the laser point cloud and the image, the limitation of single data source modeling is solved, and real-scene three-dimensional modeling with high precision, high integrity and real-time updating is realized.

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.

An intelligent pulmonary nodule grading method and system based on multi-modality feature fusion. The method comprises: using clinical multi-modality data of a physical examination crowd to acquire an ROI and a VOI of a pulmonary nodule on the basis of a chest CT examination image and an examination report, designing a multi-task feature extraction network based on an attention mechanism, and acquiring radiomics features and deep-layer image features from the ROI and the VOI; designing a cross-modality feature fusion method based on graph representation learning, designing a multi-modality information extraction method, acquiring a specific feature representation and graph structure of each modality, and then performing feature representation and graph structure fusion; and providing a pulmonary nodule grading GCN model optimization and clinical verification method based on self-supervised learning, so as to implement fine grading of pulmonary nodule malignancy having only slight differences. A new way is provided for the design of a fine-grained classification algorithm; and in the aspect of solving a clinical problem, provided is a classification algorithm optimization method and clinical verification scheme in which generative self-supervised learning and comparative self-supervised learning are organically combined.

A LIDAR-to-LIDAR alignment system includes a memory and an autonomous driving module. The memory stores first and second points based on outputs of first and second LIDAR sensors. The autonomous driving module performs a validation process to determine whether alignment of the LIDAR sensors satisfy an alignment condition. The validation process includes: aggregating the first and second points in a vehicle coordinate system to provide aggregated LIDAR points; based on the aggregated LIDAR points, performing (i) a first method including determining pitch and roll differences between the first and second LIDAR sensors, (ii) a second method including determining a yaw difference between the first and second LIDAR sensors, or (iii) point cloud registration to determine rotation and translation differences between the first and second LIDAR sensors; and based on results of the first method, the second method or the point cloud registration, determining whether the alignment condition is satisfied.

Systems and methods for automated unmanned aerial vehicle recognition. A multiplicity of receivers captures RF data and transmits the RF data to at least one node device. The at least one node device comprises a signal processing engine, a detection engine, a classification engine, and a direction finding engine. The at least one node device is configured with an artificial intelligence algorithm. The detection engine and classification engine are trained to detect and classify signals from unmanned vehicles and their controllers based on processed data from the signal processing engine. The direction finding engine is operable to provide lines of bearing for detected unmanned vehicles.

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.

The invention discloses a target identification tracking method and system based on multi-source fusion imaging, and the method comprises the steps: obtaining and preprocessing multi-modal environment monitoring data, carrying out the image enhancement based on the preprocessed multi-modal environment monitoring data, and obtaining the image-enhanced multi-modal environment monitoring data; performing multi-modal feature extraction on the image enhancement multi-modal environment monitoring data, and introducing a cross attention mechanism to fuse the extracted multi-modal features to construct cross-modal fusion features; constructing a cascade target recognition model, and inputting cross-modal fusion features to perform target recognition on the current monitoring scene; tracking a scene recognition target in the current monitoring scene to generate scene target tracking information, and storing the scene target tracking information in a preset track memory pool; and when a tracking target disconnection condition occurs during target tracking, obtaining a mismatched tracking trajectory, and performing tracking trajectory disconnection repair in combination with the trajectory memory pool. Therefore, the identification accuracy and tracking reliability of the target in the monitoring scene are improved.

The present invention provides a three-dimensional reconstruction method integrating a laser radar and oblique photography, comprising: S1, acquiring an oblique image and image data and preprocessing same to obtain a first oblique image, wherein the image data comprises POS data; S2, acquiring coordinates of a ground control point and high-precision POS data; S3, correcting the oblique image to obtain a second oblique image; S4, performing aerotriangulation on the second oblique image, and obtaining an effective matching point pair by means of improved SIFT; S5, performing combined adjustment on the basis of the effective matching point pair and performing dense matching to obtain a dense point cloud; S6, acquiring a laser point cloud and preprocessing same; S7, unifying the coordinates of the laser point cloud and the dense point cloud, and using improved ICP to obtain a fused point cloud; and S8, constructing a triangulated irregular network and performing texture mapping to generate a three-dimensional model. In the present invention, on the basis of the advantages of high resolution and strong anti-interference capability of a laser radar, defects in an oblique photography three-dimensional model are mitigated, and meanwhile, an oblique image is corrected and an algorithm used in feature matching is improved, thereby improving the precision, integrity and efficiency of the three-dimensional model.

The present disclosure belongs to the technical fields of computers and defect detection. Provided are a defect detection method and a related device. The method comprises: obtaining an image to be processed; processing said image by means of a feature extraction module in a deep-learning defect detection model, wherein the feature extraction module comprises an intermediate feature layer, the intermediate feature layer comprises a dropout layer, and the dropout layer is configured to randomly drop features which are extracted by means of the intermediate feature layer processing said image, such that a plurality of features subjected to random dropout are obtained after said image is processed by means of the feature extraction module; by means of a detection head in the deep-learning defect detection model, processing the plurality of features subjected to random dropout, so as to obtain a plurality of category prediction results and a plurality of position prediction results of defects in said image; and obtaining category detection results, position detection results, category uncertainties and position uncertainties of the defects in said image according to the plurality of category prediction results and the plurality of position prediction results of the defects. (FIG. 1)

The present invention discloses an edge-guided RGBD underwater salient object detection method with multi-attention, which includes the steps of: S1 performing channel-dimension preprocessing on RGB image and depth map and serially connecting them to obtain an RMD image; S2 inputting the RMD image into a trained salient object detection model including an encoder structure and a decoder structure; and S3 obtaining salient images and edge images output from the salient object detection model as object detection results, in which the encoder generates feature maps with reduced resolution layer by layer using the RMD image based on a residual neural network, and the decoder structure includes an edge detection branch used to fuse the feature maps layer by layer, and a saliency detection branch used to fuse the feature maps and edge feature maps layer by layer. The present invention preprocesses the input image with underwater imaging features, and uses an edge prediction image to guide multi-layer salient object detection, thereby enhancing edge details of the salient image.

The invention relates to the technical field of image processing, and discloses an intelligent machine vision detection method and system based on image processing, and a storage medium. The method comprises the steps of performing candidate region generation and geometric constraint optimization processing on fusion features through a cascade bidirectional region suggestion network to obtain a target candidate region, and performing instance segmentation processing on the target candidate region through a graph convolutional network and a boundary dynamic adjustment mechanism to obtain a segmentation result; performing feature extraction processing on the segmentation result through a space-time joint attention network to obtain space-time context features; performing multi-target tracking processing on the spatio-temporal context features through depth correlation measurement and motion mode matching to obtain a trajectory prediction result; and performing abnormal mode modeling processing on the trajectory prediction result through hierarchical contrast learning and multi-modal feature fusion to obtain abnormal features, and generating a visual inspection report according to the abnormal features. According to the invention, the accuracy of intelligent machine vision detection based on image processing is improved.

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

The invention 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.