49 results about "Feature sharing" patented technology

The invention discloses a high-precision image detection method for a micro-drill blade surface based on deep learning, and the method comprises the following steps: S1, collecting a visible light image and a structured light image of the micro-drill blade surface, and completing the image preprocessing; s2, performing spatial alignment on the image, executing cross-modal fusion, and generating a feature fusion tensor; s3, inputting the feature fusion tensor into a multi-scale residual backbone network, and extracting a hierarchical semantic feature set; s4, inputting the semantic feature set into three task branches of defect detection, region segmentation and type classification, and outputting a corresponding prediction result; s5, calculating a multi-task loss function, dynamically adjusting task branch weights, and optimizing a feature sharing structure; and S6, generating a detection report according to a prediction result, and outputting defect coordinates, a boundary contour, a type label and a confidence value. According to the method, multi-modal fusion, high-precision identification and structured output of the micro-drill blade surface are realized, and the accuracy, efficiency and automation level of defect detection are remarkably improved.

The invention discloses a Conformer-based multi-task wireless communication signal classification method, and belongs to the technical field of crossing of signal processing and artificial intelligence. In order to solve the problems of task modeling isolation, insufficient feature sharing mechanism and weak model generalization ability in existing wireless communication signal classification, the method comprises the following steps: acquiring an IQ signal, performing dimension raising through a front-end convolution module, inputting the IQ signal into a Conformer network fusing local convolution and a global attention mechanism, and performing depth time sequence feature extraction; and synchronously realizing discrimination of a signal-to-noise ratio grade, a channel type, a modulation mode and a communication system by using a parallel multi-task classification head. The method can improve the classification accuracy and the model generalization ability, reduces the consumption of computing resources, and is suitable for signal recognition in wireless communication, radar and Internet of Things systems.

The invention relates to a deep feature fusion-based fabricated component surface defect multi-task detection method. The method comprises the following steps of: constructing a multi-view camera array and an illumination feedback regulation and control module at a data acquisition end; on the algorithm level, feature cross-layer propagation and information compensation are realized through an improved lightweight convolutional network and a multi-scale residual diffusion module; a dynamic feature fusion module is introduced, and a self-adaptive fusion weight is generated based on channel statistical features, so that feature sharing and differential expression are realized among different tasks; meanwhile, a defect perception attention mechanism and cross-task consistency constraint are adopted, and the problem that semantic space distribution is inconsistent in the multi-task detection process is solved; in the detection post-processing stage, a three-dimensional quantitative evaluation system based on the geometric dimension, the texture roughness and the depth volume is constructed, and unified grade evaluation of the surface defects of the component is achieved through the multi-feature fusion quality index. The problems of low detection efficiency, unstable precision, difficulty in collaborative recognition of multiple types of defects and the like in existing component delivery detection are solved.

The invention discloses a personnel behavior recognition method and device in a low-illumination scene and a medium, relates to the technical field of image processing, and aims to integrate image enhancement and attitude estimation into a behavior recognition model to solve the recognition difficulty in a low-illumination condition. Three tasks of image enhancement, human body posture estimation and personnel behavior recognition are integrated, multi-task neural network model training is carried out based on posture weighted global pooling consistency and posture-driven background random suppression, and through feature sharing and task cooperative training, the human body posture estimation and personnel behavior recognition are realized. According to the method, the problem of low accuracy of personnel behavior recognition in low-illumination monitoring environments such as oil fields can be solved, particularly for the conditions of dark and fuzzy images and changeable human body postures, the utilization of posture information is improved, the robustness and precision of personnel behavior recognition in complex environments are improved, and end-to-end optimization is realized.

The invention relates to the technical field of commodity value evaluation, in particular to a commodity value evaluation model construction method and system based on a small sample scene. Comprising the following steps: establishing a plurality of sub-data sources according to equipment category parameters, and constructing a federal collaboration layer according to all the sub-data sources; acquiring a first-level shared feature packet generated by the federal collaboration layer, correcting the first-level shared feature packet according to a preset transfer learning layer, and generating a second-level feature packet according to a correction result; an initial evaluation model is constructed according to the secondary feature package, and a commodity evaluation model is constructed by the time sequence layer according to the initial evaluation model; a plurality of sub-data sources are established on the basis of equipment categories, a feature space alignment mechanism of cross-category equipment is established by establishing a mapping transmission model of each sub-data source, local feature sharing of different equipment categories is realized through federated learning, and the number of dependencies of evaluation and cross-category evaluation errors of the model are reduced.

The invention relates to a multi-task lightweight detection method for factory appearance defects of fabricated concrete members. The method comprises the steps of image data acquisition and preprocessing and lightweight multi-task detection model construction. The lightweight multi-task detection model construction process comprises a feature extraction module, a task branch module, a feature fusion module and an attention optimization module, the model design follows the principle of lightweight parameter compression, multi-task feature sharing and deep semantic fusion, and recognition and quantification of multiple types of appearance defects can be completed in a single network at the same time. According to the method, real-time, accurate and integrated detection of multiple defects on the surface of the component is realized, so that the industrial application requirement of factory detection of a component factory is met, the detection quality and efficiency are remarkably improved while the detection cost is reduced, and a more efficient and more reliable technical means is provided for factory quality control of the fabricated concrete component.

The invention provides a business data feature sharing method and system based on machine learning, and the method comprises the steps: firstly obtaining a business link feature set of a talent service field, including a personnel core, post association and service process features, and a business demand description of a feature calling main body; then constructing a feature anchoring rule to perform association processing to obtain a feature anchoring result, inputting the feature anchoring result into a pre-training machine learning model to calculate a shared adaptation parameter, and constructing a shared execution link including feature extraction, processing and delivery nodes according to the shared adaptation parameter; and carrying out association mapping on the features output by the sharing execution link and a talent service business system of the feature calling main body, generating a feature sharing result containing a plurality of identifiers, and feeding back the feature sharing result to the business system to support business decision operation, thereby realizing efficient and accurate sharing of talent service business data features.

This invention relates to the field of artificial intelligence technology and discloses an obstacle detection method and system based on unmanned mining trucks. One obstacle detection method for unmanned mining trucks includes: collecting and preprocessing vehicle data; constructing a dust and fog evolution prediction network to obtain a defogging multispectral image; extracting the distribution information of different materials in the scene to form an enhanced scene feature representation; performing adaptive fusion and uncertainty evaluation to obtain a gating feature representation; using an obstacle detection model to detect obstacles; and constructing a multidimensional occupancy grid to output multidimensional obstacle data. This invention overcomes the performance bottleneck of traditional defogging algorithms in heavy dust and fog environments in mining areas by decoupling from the dust and fog environment through physical modeling. By constructing a multi-vehicle collaborative perception network with sparse feature sharing, it overcomes the problems of blind spots and insufficient long-distance perception capabilities in single-vehicle perception, enhancing the system's detection reliability in dynamic and complex environments and ensuring the safety of autonomous driving operations in mining areas.

This invention discloses a lightweight method and system for detecting small targets on UAVs in complex scenarios, relating to the field of target detection technology. It acquires image data captured during UAV flight and annotates the targets to construct a standardized dataset for training and validation. Based on this, the images undergo size normalization and left / right block cropping to form candidate detection regions suitable for small target detection. These candidate detection regions are then input into a small target detection model built on an improved YOLOv5s platform. Through lightweight feature extraction, multi-scale feature fusion, and detection result mapping and filtering, the model outputs detection results containing target category information, target bounding box position, and target confidence. Simultaneously, by introducing a cross-regional collaborative enhancement mechanism, the semantic constraints and feature sharing between block regions are strengthened, thereby improving the continuity, stability, and lightweight deployment adaptability of UAV small target detection in complex backgrounds.

The application discloses a system and method for synchronously detecting epilepsy attack and type identification, and the system comprises an electroencephalogram signal processing module, a multi-scale time-space-frequency feature electroencephalogram fusion module, an epilepsy attack detection task branch, a cross-task attention interaction module, an epilepsy attack type classification branch and a multi-task learning optimization module; the cross-task attention interaction module realizes bidirectional feature sharing between the task branches by establishing an information interaction mechanism at a feature level, so that the epilepsy attack detection task can utilize fine-grained structural information related to attack types to improve detection precision, and meanwhile, the attack type classification task can utilize attack time positioning and context information provided by the detection task; the application realizes collaborative modeling of epilepsy attack detection and attack type classification, improves the accuracy, robustness and clinical application value of epilepsy attack identification by sharing feature representation and cross-task information interaction, while ensuring the calculation efficiency.

Aspects relate to techniques for visual feature sharing between wireless communication devices for relative pose determination. A first wireless communication device may transmit a request for visual feature sharing to a second wireless communication device and in response receive a message from the second wireless communication device including a plurality of features (e.g., keypoints) of an image captured by the second wireless communication device. The first wireless communication device may then calculate a relative pose of the first wireless communication device with respect to the second wireless communication device based on an association between the features provided by the second wireless communication device and additional features obtained from an additional image captured by the first wireless communication device.

The invention provides a deep collaborative multi-task feature learning method based on mutual information regularization. Aiming at the technical problems of low feature sharing efficiency, serious inter-task interference, poor generalization ability and the like in the existing multi-task learning, the invention constructs a deep learning framework based on mutual information constraint, and designs a mutual information calculation module of variational inference and a KL divergence regularization optimization mechanism. And cross-task feature efficient sharing is realized through a multi-layer encoder structure and a feature adaptive selection mechanism. According to the scheme, mutual information calculation is optimized by adopting a batch estimation strategy and a parallel calculation mechanism, and a dynamic task weight distribution and cross-task knowledge migration mechanism is introduced to enhance the generalization ability of the model. Experiments show that compared with the prior art, the classification accuracy, convergence speed, calculation efficiency and the like of the method are improved by 31.2%, 65.3% and 44.8% respectively, and the method has higher cross-domain migration ability and can be widely applied to multi-task learning scenes such as computer vision and natural language processing.

The application discloses a kind of instance segmentation and target detection hybrid identification method based on multi-feature fusion, belong to computer vision technical field.The method constructs multi-task fusion network;Through the feature extraction of image to be measured of main network, shared fusion feature map M1 is obtained by multi-scale feature pyramid fusion;It is respectively input into target detection and instance segmentation module, and detection result and segmentation result are obtained;Then feature map M1, target detection result and instance segmentation result are jointly input into posture estimation module, and posture estimation result is obtained;Three kinds of results are uniformly mapped and aligned in space, are spliced and weightedly fused in channel dimension, are input into fully connected layer learning task weight combination, and the identification result containing target class, spatial position, pixel-level segmentation mask and key point coordinate is output.The application is collaboratively designed by feature sharing, multi-task fusion and cross-frame matching mechanism, to improve system operation efficiency while ensuring identification accuracy.

The application discloses a remote sensing image segmentation method based on state space dual transformation and channel mixing. Firstly, dimension reduction and feature extraction are performed on the input image to generate reduced dimension features, which are further compressed to represent hidden states. These features are mapped into the hidden state space by using non-causal state space dual transformation, and the information interaction is optimized by channel mixing to generate hidden state features. Secondly, the reduced dimension features and the hidden state features are respectively reduced in dimension, then mixed in channels, and added together to fuse global information. Channel displacement is applied to enhance the feature sharing capability between different modalities, and after re-channel mixing and local spatial information extraction, the final enhanced features are generated. Finally, based on the enhanced features, the segmentation task is performed to output high-precision segmentation results. The application optimizes the calculation efficiency and feature fusion, and improves the precision and robustness of remote sensing image segmentation.

This invention proposes a depth and 3D keypoint estimation method for intelligent accompanying patrol vehicles: depth estimation is achieved end-to-end from binocular infrared images, while simultaneously identifying the detected region from the depth map and performing 3D keypoint recognition of the target. The corrected left and right viewpoint images are jointly processed by a parameter-shared feature extraction module, then respectively enter the depth estimation branch for depth estimation and the keypoint branch for 3D keypoint recognition. This invention enables end-to-end estimation from binocular infrared images to depth maps and 3D keypoints, avoiding cumbersome intermediate processes and improving the computational and operational complexity of depth map estimation and 3D keypoint recognition. Through feature sharing, joint constraints are achieved in the parameter learning process between branches, improving the inference accuracy of each task. This invention also improves the efficiency of matching cost aggregation in the depth estimation task and the coordinate calculation efficiency in the keypoint estimation task.

This invention discloses a method, device, and medium for human behavior recognition in low-light scenarios, relating to the field of image processing technology. It integrates image enhancement and pose estimation into a behavior recognition model to address the difficulties in recognition under low-light conditions. By proposing a multi-task joint neural network model, it integrates image enhancement, human pose estimation, and human behavior recognition into one system. The multi-task neural network model is trained based on pose-weighted global pooling consistency and pose-driven background random suppression. Through feature sharing and task collaborative training, it can solve the problem of low accuracy in human behavior recognition in low-light monitoring environments such as oil fields. It is particularly effective in situations where images are dim and blurry, and human poses are variable, improving the utilization of pose information and enhancing the robustness and accuracy of human behavior recognition in complex environments, achieving end-to-end optimization.

The embodiment of the invention provides a feature sharing method and device based on multi-model parallel reasoning and a related product, and the method comprises the steps: determining an expected feature which needs to be called by a first target object recognition model; determining at least one second target object recognition model capable of generating expected features and a target layer used for generating the expected features in the second target object recognition model; generating a first hash value of the expected feature according to the target video frame, the model identifier of the second target object recognition model and the layer depth information of the target layer; acquiring first semantic information of the first target object recognition model for the expected features, and retrieving whether shared features corresponding to the expected features are pre-stored in a shared feature library or not according to the first hash value and the first semantic information; and if yes, calling the shared features through the first target object recognition model, and if not, extracting the expected features from the target video frame through the first target object recognition model.

The application discloses a kind of transmission line icing detection methods based on wide-area perception dynamic convolution model, first, multi-dimensional attention module (Multi-Dimensional Attention Module, MDAM) is used to optimize the feature sharing between convolution kernel, more accurately obtain the detailed information of small target;Second, a kind of Thin-head feature extraction network fusing lightweight model (Lightweight model, LM) is proposed, which retains the hidden connection of spatial feature information gradually transmitted to the channel;Using a loss function (Shape-NWD) that combines attention shape cross ratio with normalization, reduces the underfitting caused by positive and negative sample imbalance.Finally, the experiment fully proves the efficiency of the method of the application.

The application discloses a high-precision image detection method for micro drill blade surface based on deep learning, comprising the following steps: S1, collecting visible light images and structured light images of the micro drill blade surface, and completing image preprocessing; S2, performing spatial alignment on the images, executing cross-modal fusion, and generating a feature fusion tensor; S3, inputting the feature fusion tensor into a multi-scale residual backbone network to extract a layered semantic feature set; S4, inputting the semantic feature set into three task branches of defect detection, region segmentation and type classification, and outputting corresponding prediction results; S5, calculating a multi-task loss function, dynamically adjusting the task branch weight, and optimizing the feature sharing structure; S6, generating a detection report according to the prediction results, and outputting defect coordinates, boundary contours, type labels and confidence values. The application realizes multi-modal fusion, high-precision identification and structured output of the micro drill blade surface, and significantly improves the accuracy, efficiency and automation level of defect detection.

The invention relates to the technical field of intelligent inspection, and provides a multi-task power transmission line defect detection algorithm based on feature sharing. The objective of the invention is to solve the technical problems of low power transmission line defect detection precision, high false alarm rate and weak model generalization ability in the unmanned aerial vehicle inspection process. The main scheme comprises the steps of collecting RGB image data of a power transmission line through an unmanned aerial vehicle; performing device-level target identification by adopting a DINO target detection model, and outputting a position frame and a category array containing normal devices and defects; clustering according to a target center point by using a K-means clustering algorithm, combining the cut areas in combination with a pre-adaptive window size threshold, and outputting cut target image stacks to optimize scale processing; inputting the cut image into a shared feature extraction network, respectively outputting defect detection results of different device categories through a plurality of independent detection heads, and improving the distinguishing capability by using multi-task learning; according to result mapping, the detection result is mapped back to an original image according to cutting frame coordinates, and final defect positioning and classified output are generated.

The invention discloses a mediastinal region lymph node metastasis identification method and system based on a double-control routing hybrid expert model, and belongs to the technical field of computer intelligent auxiliary diagnosis. The invention aims to solve the problems that the existing mediastinal region lymph node metastasis identification method is low in accuracy and poor in identification effect due to the fact that the mediastinal region lymph node metastasis identification task is complicated and includes multi-dimensional information identification. According to the technical key points, a mediastinal region lymph node metastasis identification task is split into two auxiliary sub-tasks of mediastinal region detection and lymph node metastasis prediction, and the three tasks jointly form a multi-task learning system. Knowledge transmission and complementation are realized through feature sharing between tasks and gradient-based collaborative optimization, so that the feature distinguishability of each task is improved. The method is characterized in that a double-control routing gating mechanism is constructed, similar features and unique features between different tasks are extracted through a feature routing branch and a task routing branch respectively, and correlation and difference between the tasks are balanced; meanwhile, a two-dimensional gradient balance algorithm is designed, multi-task gradient optimization is carried out from two angles of gradient direction alignment and amplitude dynamic balance, and the problems of gradient conflicts and task dominance are solved while gradient feature distribution between tasks is kept.

Described herein are devices, systems, methods, and processes for intelligently managing power consumption in a network by allocating a power budget for packet processing. The power budget can be allocated based on criticality and / or the trust level of the flow. A network device may determine which subsets of features can be executed within the power budget for specific flows. Network devices can signal their capability to run features based on power consumption and adherence to the power budget, allowing for cooperative end-to-end power-based decision-making and policy enforcement. Network devices unable to run all features can select a subset of the features within their power budget and a viable path where other network devices can execute the missing features. Source route information can be added to indicate the path and missing features to be executed by network devices down the segment routing path.

This invention discloses a PCB defect detection method and system based on an adaptive multi-sub-model. The method involves acquiring images of the PCB surface using a high-resolution camera and preprocessing the images; using reinforcement learning for dynamic priority decision-making, rationally selecting the priority of sub-models based on equipment status and computational resource load; employing YOLOv8 as the backbone network, with multiple sub-models working in parallel and trained using gradient masks; implementing multi-level cascaded detection through confidence thresholding and variance analysis, and optimizing model parameters based on batch statistical results; and optimizing the sub-models through incremental training based on the recall and false positive rates of each detection. This invention proposes an adaptive multi-sub-model, which, through dynamic priority control and feature sharing mechanisms, dynamically adjusts the detection strategy while ensuring high accuracy, reducing computational burden and achieving efficient and accurate PCB defect detection.

The application discloses a kind of multi-modal multi-task workshop target identification methods, including constructing sample data set, wherein the data sample contains a group of color pictures and depth pictures, and carries out target detection level and instance segmentation level annotation;Multi-modal multi-task workshop target identification network is constructed;Multi-modal multi-task workshop target identification network training;Multi-modal multi-task workshop target identification network task identification.In the application, two ResNet50 backbone networks are used in the construction of multi-modal multi-task workshop target identification network, four fusion modules are set between the two ResNet50 backbone networks, two task branches are used in the decoding part, and three feature sharing modules are set between the two task branches.The application has good recognition accuracy for color similar targets in the workshop scene, and can realize instance segmentation and target detection in the same scene.

The application discloses a lung nodule detection and semantic attribute rating method based on multi-task learning, which comprises two sub-tasks of lung nodule detection and semantic attribute rating. In order to realize feature sharing of joint learning between the two sub-tasks, the application connects a lung nodule detection sub-network and a semantic attribute rating sub-network together to form an end-to-end joint model. The lung nodule detection sub-network acquires position information of the nodule, and the output thereof serves as input of the semantic attribute rating sub-network. The two sub-networks share bottom layer features in a down-sampling stage of a U-Net network, so that feature sharing of the multi-task model is realized. In the process of joint learning training of the two tasks, since the training difficulty and convergence speed of different sub-tasks can be different, the application adopts a dynamic weight average method to adjust loss weights of different tasks. The method can not only effectively detect lung nodules, but also identify semantic attributes of the lung nodules as an additional supervision signal.

The invention provides a cross-task countermeasure attack method and system for an automatic driving scene, and a storage medium. The method comprises the following steps: S1, data preprocessing: carrying out image standardization and resolution compatibility processing on collected original image data; s2, on the basis of key features of different perception task models, optimization of an adversarial sample is carried out, the optimization comprises the following processes of feature extraction, feature damage loss, feature sharing fusion and feature-level disturbance optimization, and through multiple times of iterative optimization, the adversarial sample of multi-perception task depth feature damage is finally obtained; and S3, further optimizing and generating a final confrontation sample on the basis of the feature-level disturbance generated in the step S2 through a method of constructing a self-adaptive task weight. The method has the beneficial effects that a cross-task adversarial attack is provided for a complex system in which a plurality of sensing models cooperate, so that different sensing modules of the system can identify errors at the same time.

The invention relates to the field of unmanned vehicle control, and discloses a vehicle end-to-end control method and system, a storage medium and computing equipment, and the method comprises the steps: collecting a single-path RGB image and a measurement vector of a running vehicle in real time through a road simulation map constructed by a CARLA simulator, and constructing a data set after preprocessing; inputting the single-path RGB image features into a GCViT image feature extraction model based on a cross attention mechanism, realizing space interaction and feature sharing through local and global self-attention calculation, and outputting image features; and constructing a control action prediction model based on reinforcement learning, training an RL expert by using a Roach model, and outputting vehicle control parameters through an IL agent by taking the image features and the measurement vectors as input. According to the invention, safe automatic driving in a simple driving environment can be realized, and robustness and real-time performance in scenes such as a temporary cone barrel road and the like are improved.

The application provides a feature sharing-based unknown target detection method and a storage medium, wherein first, sample image data is obtained and classified; then, attribute decomposition is performed on the sample image data according to the category of the sample image data, a plurality of attribute features are obtained, and different attribute labels are assigned to each attribute feature; then, all attribute features and attribute labels are input into a detection model for training to obtain a trained detection model; and then, a to-be-detected image is input into the trained detection model to output a detection result. Since the sample image data is decomposed according to attributes, each sample image data adaptively generates one or more attribute features, thereby effectively widening the sample quantity. Meanwhile, each attribute feature is subjected to recognition training, so that the trained detection model judges the category of the to-be-detected image, thereby effectively improving the recognition speed of target detection.

This invention provides a cross-task adversarial attack method, system, and storage medium for autonomous driving scenarios. The method includes: Step S1, data preprocessing: performing image standardization and resolution compatibility processing on the collected raw image data; Step S2: optimizing adversarial examples based on the key features of different perception task models, including the following processes: feature extraction, feature destruction loss, feature sharing fusion, and feature-level perturbation optimization. Through multiple iterative optimizations, adversarial examples with deep feature destruction for multiple perception tasks are finally obtained; Step S3: further optimizing and generating the final adversarial examples based on the feature-level perturbations generated in Step S2 by constructing adaptive task weights. The beneficial effect of this invention is that it aims to propose a cross-task adversarial attack for complex systems with multiple perception models cooperating, enabling different perception modules of the system to simultaneously identify errors.

The present application relates to a kind of light YOLO network and method for solar panel damage detection.The network includes: backbone network is configured as feature extraction, it includes embedding multiscale dilation convolution C3k2_MDC module, the C3k2_MDC module is configured as expanding receptive field and extracting multiscale feature;Neck network is configured as feature fusion, it includes the C3k2_AFFM module integrated attention mechanism, the C3k2_AFFM module is configured as performing weighted fusion to the multiscale feature;Detection head is configured as based on the feature output damage detection result after fusion, it includes multi-feature sharing detection head MSDH.The network and method proposed in the present application can accelerate convergence speed, reduce parameter quantity, and significantly improve detection precision and operating efficiency.