8results about How to "Distinctive features" patented technology

Embodiments of the present application relate to the technical field of human-computer interaction, and disclose a method and device for determining an interactive gesture and an electronic device. The method comprises the following steps: first, target component detection is performed; then, gesture detection and recognition are performed in a local area (around a boundary box) near the target component. In addition, for N gesture detection boxes (multiple users) in a current video frame, M target gesture detection boxes are selected therefrom to perform hand component positioning and gesture recognition processing, which can reduce detection time consumption and improve real-time performance. On this basis, the interactive gesture is determined according to the gesture detection result of the current video frame and the gesture detection results of a plurality of historical video frames, that is, gesture recognition is selectively performed on the user in each frame in a plurality of continuous video frames to determine the interactive gesture, which can reduce the computing power, reduce the time consumption, improve the real-time performance, and improve the accuracy and stability of the interactive gesture. That is, in the multi-user human-computer interaction scene, the interactive gesture can be accurately and timely determined.

The application discloses a kind of based on double filtering cooperation's remote sensing image scene classification method and system, in the method, for the problem that existing technology is difficult to give consideration to background noise suppression and key feature edge structure preservation when processing remote sensing image, a kind of double filtering cooperation optimization module is presented.The module in this paper introduces Gaussian filter smoothing channel to suppress unstructured high-frequency background noise, while introducing Gaussian Laplace edge enhancement channel to accurately capture and strengthen key geometric structure information such as feature contour;Through feature fusion, position coding, state space model and double activation gating mechanism, the multi-scale feature map is optimized and reconstructed to generate high signal-to-noise ratio and clear structure scene representation.The module is integrated into the remote sensing image scene classification model, and trained using focal loss, which can significantly improve the classification accuracy and robustness in complex texture interference scene.

The application provides a novel feature up-sampling method for semantic segmentation, which improves the resolution of an image by combining pixel reorganization Pixel Shuffle with an attention mechanism, prevents a chessboard effect, and avoids a 0 filling operation on unknown pixel points compared with a previous traditional up-sampling module; secondly, the attention mechanism is introduced to combine global information with local information, obtain a feature map with a larger receptive field and more detailed information, increase effective feature information, and realize the function of effectively improving the segmentation performance of the network. The application has universality, eliminates the limitations of previous methods by simply replacing all up-sampling modules, obtains more representative feature mappings, obtains an enlarged image with good features, makes the restored image obtain more feature information, and is beneficial to improving the segmentation result of the network model.

The present application relates to a kind of urban green space scene laser radar point cloud single tree segmentation method, the method comprises: the point cloud of urban green space is carried out grid cubic processing;From the initial trunk position in grid is determined to fit DEM elevation plane, from DEM elevation plane upwards cutting tree bottom point to determine tree position;Using K-means clustering to carry out rough segmentation;Then according to single tree position, establish tree adjacent relationship, using ring extraction to carry out single tree optimization segmentation.The present application can make the trunk point cloud feature more obvious compared to tree crown point cloud, and the ground elevation information of the position where the trunk is located can be obtained, and compared with clustering time efficiency, it is based on the trunk point cloud and elevation information obtained to carry out a series of operations to complete rough single tree segmentation, and using adjacent relationship and ring extraction to optimize segmentation result, can make the accuracy of identification result higher.

The utility model discloses a functional all terrain vehicle high-safety integrated LED tail lamp. The functional all terrain vehicle high-safety integrated LED tail lamp comprises a tail lamp base, a tail lamp circuit board, a tail lamp middle cover and a tail lamp cover. The tail lamp base is fixed to a frame of the all terrain vehicle, a lamp panel mounting position is arranged on the tail lamp base, and the tail lamp circuit board is fixed to the lamp panel mounting position. The rear tail lamp middle cover is arranged on the rear tail lamp base in a buckling mode through a buckling structure and shields the rear tail lamp circuit board. The rear tail lamp lampshade is arranged on the rear tail lamp base through a buckling structure and shields the rear tail lamp middle cover, and the waterproof effect is achieved through the rear tail lamp lampshade. The sealing rubber ring between the tail lamp shade and the base is combined with the closed cavity formed by the buckling structure, so that water vapor and dust are effectively prevented from invading the internal circuit board, and the reliability and the long service life of the lamp under the severe working condition of the dune buggy are ensured.

The invention relates to the technical field of photovoltaic station operation and maintenance management, and discloses a two-stage photovoltaic panel identification method and device based on deep learning, and the method comprises the steps: obtaining a target photovoltaic string image, carrying out the detection of a photovoltaic string through a deep learning model based on the target photovoltaic string image, and generating an ROI image corresponding to the target photovoltaic string; performing edge detection on the ROI image to obtain an edge contour of the photovoltaic panel; and traversing the edge contours of the photovoltaic panels, and screening and identifying the edge contours of the photovoltaic panels to obtain a global photovoltaic panel corresponding to the target photovoltaic string. According to the invention, the statistical accuracy and real-time performance of the photovoltaic panel are remarkably improved, and core technical support is provided for construction management and intelligent operation and maintenance of a photovoltaic field station.

The utility model relates to the technical field of oral medical treatment, in particular to an implant body posture auxiliary positioning piece and an implant body posture auxiliary positioning kit. The implant posture auxiliary positioning piece comprises an auxiliary positioning part, a signal reflection structure is arranged on the auxiliary positioning part, the signal reflection structure comprises a center circle and four corner circles, the four corner circles are located at the four vertex positions of the same rectangle respectively, and the center of the rectangle coincides with the circle center of the center circle; the signal reflection structure further comprises a rectangular frame defined by closed lines, and the center circle and the four corner circles are located in the rectangular frame. A plurality of protrusions extending towards the center circle direction are arranged on the inner side of the rectangular frame. According to the utility model, the serial number of the implant body posture auxiliary positioning piece can be quickly identified.

The invention belongs to an underground cable fault positioning method, and provides a traveling wave time delay estimation method and a related device in order to solve the problem that a traveling wave signal processing algorithm cannot completely meet high-precision and high-adaptability traveling wave time delay detection requirements in a complex electric power scene. Carrying out adaptive frequency band wavelet deconstruction and detail extraction on the first sensing signal and the second sensing signal respectively, and then carrying out dynamic parameter modal splitting based on variational problem solution on each reconstructed signal in the obtained multi-layer first reconstructed signal group and the second reconstructed signal group corresponding to the scale; the method comprises the following steps: obtaining a plurality of intrinsic frequency band modal components with specific sparsity, taking the first intrinsic frequency band modal component as a main mode, comprehensively considering the energy ratio and cross-correlation sharpness of two main modes in each layer, and determining a final traveling wave time delay estimation result according to the cross correlation coefficient of the two main modes under the optimal common decomposition layer. According to the method, the calculation redundancy is reduced while the precision is ensured, and the real-time performance is improved.