8results about How to "Guaranteed recognition efficiency" patented technology

This invention relates to the field of robot interaction technology, specifically to a method for recognizing the intensity of human-computer interaction intentions in real time. The method includes the following steps: S1, acquiring video of pedestrians through a collection unit mounted on the robot, performing frame extraction on the video, and using a 3D skeleton extraction model to extract the skeleton information of each pedestrian in each frame in chronological order; S2, based on the skeleton information of each pedestrian in each frame, using a preset lightweight motion behavior recognition model to identify the interaction reference information of each pedestrian; S3, analyzing and processing the three types of interaction reference information—straight-line distance, orientation information, and motion behavior—to obtain the real-time interaction intention intensity of the pedestrian relative to the robot. This invention can accurately identify the interaction intentions of pedestrians, thereby improving the initiative and naturalness of social robots when interacting with humans.

This application discloses a character recognition method. The method includes: performing text detection on a target image to obtain at least one polygonal region; determining a target polygonal region from the at least one polygonal region, wherein the target polygonal region has an irregular shape; determining four target vertices from multiple vertices of the target polygonal region, and using these four target vertices as the four vertices of a rectangular region corresponding to the target polygonal region; mapping pixel information of each pixel in the target polygonal region to the rectangular region based on the positions of each vertex of the rectangular region in the target image to generate pixel information of each pixel in the rectangular region; and performing OCR processing on the rectangular region and the polygonal regions in the at least one polygonal region other than the target polygonal region to obtain the character recognition result of the target image. This application embodiment can simultaneously achieve both accuracy and efficiency in character recognition.

This application discloses a text recognition method, apparatus, electronic device, and storage medium. This invention can be applied to various scenarios such as cloud technology, artificial intelligence, intelligent transportation, and assisted driving. This application can acquire a text image; perform convolution processing on the text image to obtain a first feature; perform dilated convolution processing on the text image to obtain a second feature; fuse the first feature and the second feature to obtain a fused feature; and recognize the text content in the text image based on the fused feature. In this application, convolution can extract the spatially denser (smaller scale) first feature, while dilated convolution can extract the spatially sparser (larger scale) second feature. By fusing these two features, a fused feature that balances spatial continuity can be obtained. Therefore, recognizing the text content in a text image using this fused feature can improve recognition accuracy.

The application provides a sensitive information identification method and device, network equipment, a medium and a program product. The method comprises: performing semantic coding processing on a target text to obtain a global semantic representation of the target text and a context vector representation of a word element; performing classification identification processing on sensitive information in the target text according to the global semantic representation to obtain a category identification result; performing sequence labeling processing on the sensitive information in the target text according to the context vector representation to obtain a first identification result; performing sequence labeling processing on the sensitive information in the target text according to the context vector representation to obtain a first identification result of the sensitive information in the target text; and cross- verifying the category of the sensitive information in the first identification result and the category identification result to update the first identification result. The method provided in the embodiments of the application has high identification accuracy and efficiency for sensitive information.

The invention discloses a page display method and device, a medium, equipment and a computer program product, and the method comprises the steps: responding to a received display request of a target page, obtaining the physical pixel density corresponding to target equipment, and obtaining the size information of an identification code in the target page, the physical pixel density is used for representing the number of physical pixels corresponding to each inch; based on the physical pixel density and the size information, determining a logic resolution corresponding to the identification code; and based on the logic resolution, displaying an identification code corresponding to the target page in an identification code area of the target page. Therefore, the same identification code can be displayed in the same physical size in different devices or in different display modes of the same device, so that the influence of adjustment of the devices or the display modes on the identification efficiency of the identification code can be avoided; and meanwhile, the scene that the identification cannot be realized or the identification is wrong due to the change of the display size of the identification code can be avoided.

The present application belongs to the technical field of neural network lightweight design and target detection tracking neural network fast inference framework, and discloses a lightweight commodity identification and tracking system, method, storage medium, equipment and terminal, which collects and labels commodity identification training data set through installation of picture collection facilities in actual intelligent vending scene; designs a backbone feature extraction network based on Ghost Module, designs a multi-scale commodity detection module based on RFP, and designs a multi-target detection and target occlusion identification module based on DIOU_NMS algorithm; adds a commodity tracking algorithm in the lightweight commodity identification network to analyze the behavior of the user, and discriminates the user's taking and abnormal operation based on continuous key frames. The present application provides a lightweight commodity identification and tracking fusion network, which can provide suggestion guidance for embedded application of network deployment in edge intelligent equipment in corresponding scenes.

The invention discloses an osteosarcoma detection method and system based on multi-target collaborative recognition, and relates to the technical field of target recognition. The method comprises the following steps: firstly, acquiring multi-modal target point data of a target image area, performing feature coding processing to obtain a multi-target point feature vector, performing adaptability verification in a multi-target point combination process, and then performing abnormal coding vector identification on a gene target point vector in a corresponding target point combination after the adaptability verification is qualified, so as to obtain a multi-target point feature vector. And finally, carrying out gene target specific binding detection and non-linear monitoring on a detection signal amplification and response process to judge whether a detection result is output, so as to realize the purpose that in the multi-target identification process, the detection result is accurately detected, and the detection accuracy is improved. Through accurate cooperative processing of serum, image and gene cross-modal data, an effective data support is provided for accurate recognition of osteosarcoma mutation sites and an osteosarcoma detection process.

This invention discloses a cross-layer semantic fusion traffic identification method, system, device, and medium, relating to the field of network security. The method includes collecting raw data from the network observation side and the semantic side and converting it into encrypted traffic vectors and log vectors carrying consistent timestamps; mapping the log vectors and encrypted traffic vectors to a unified space; calculating the semantic similarity between the log vectors and encrypted traffic vectors; constructing a time proximity weight based on the consistent timestamps; coordinating the time proximity weights and semantic similarity to generate a cross-layer association matrix; constructing a cross-layer heterogeneous inference graph using the unified vector representation and the cross-layer association matrix; mapping the target directed path extracted from the cross-layer heterogeneous inference graph to a target state vector; matching the target state vector with a reference vector; and determining the encrypted traffic identification result based on the matching result to improve the reliability of encrypted network communication.