33results about How to "Improve recall rate" patented technology

The application discloses a similar video detection method and device, belonging to the technical field of artificial intelligence. The method includes: obtaining a feature embedding vector of the first video; comparing and recalling the feature embedding vector of the first video with at least one feature embedding vector of the second video to obtain a recall video associated with the first video; The first video and the recalled video are checked and verified, and the feature verification results of the first video and the recalled video in at least one verification dimension are determined; the video feature data of the first video and the recalled video are respectively determined in the target verification dimension. In the technical solution provided by the embodiment of the present application, by decoupling the video recall process and the video similarity verification process, the video recall is first performed based on the feature embedding vector of the video, and after the recalled video is obtained, the first video is checked from multiple verification dimensions. The first video and the recalled video are reviewed and verified more accurately, and then the similar video judgment is completed, which effectively improves the efficiency of video duplication.

The invention discloses an EEG seizure detection method based on depth channel attention perception, which belongs to the fields of biomedical engineering and machine learning. The present invention introduces an attention mechanism into multi-channel EEG epileptic seizure detection, and trains an end-to-end deep channel attention perception model. The model can not only extract the deep features of the EEG signal, but also learn the contribution scores of each channel to the detection of epilepsy at the same time, so as to realize the dynamic selection of the most relevant EEG channel. Compared with the prior art, the present invention dynamically selects the most relevant EEG channel and expresses epilepsy features cooperatively by combining deep feature extraction and attention mechanism, so that its fusion features have channel-aware ability, and can improve the detection rate of epilepsy while improving the epilepsy detection rate. explanatory.

The invention discloses a construction system and method of a knowledge map, and belongs to the technical fields of natural language processing (NLP) and computer information processing. The system includes: a crawler module, which carries out crawling and data cleaning on text; a basic labeling module, which is used for carrying out basic labeling work including subject completion operations; a candidate relationship extraction module, which is used for extracting candidate relationships including candidate relationship sentences and / or relationship entity pairs, a feature extraction module,which is used for carrying out feature extraction; a relationship classifier training module, which is used for carrying out model training according to candidate relationship extraction results and feature extraction results to construct a relationship classifier; and a relationship audit module, which is used for carrying out audit determination on candidate sentence relationships obtained by the relationship classifier, and accordingly adjusting the relationship classifier according to results of audit determination. The system realizes higher relationship extraction capability, reduces costs of manual participation, and improves efficiency of constructing the knowledge map.

The present application proposes a lane marking detection method, device, electronic equipment and storage medium. Wherein, the method includes: inputting the acquired image to be detected into a preset target detection model, obtaining first detection information and second detection information of each grid in the image; Second, the detection information is subjected to non-maximum value suppression processing to obtain the position and category of each lane line boundary point in the image, the position of each lane center point, the corresponding lane width and the lane line category; according to each lane line boundary point in the image The location and category of each lane center point, the lane width corresponding to each lane center point, and the lane line category determine the lane line in the image and the category of each lane line. Therefore, through this lane line detection method, not only the accuracy and accuracy of lane line detection are improved, but also the robustness and accuracy of lane line type detection are improved.

The present application relates to the technical field of automatic driving, and provides a parking space recognition method, device, vehicle-mounted terminal and storage medium. In this application, the corner points of parking spaces are used as the core points of density clustering. Since adjacent corner points of parking spaces are connected by parking space lines, that is, there are detection points of parking space lines between adjacent corner points of parking spaces. Therefore, based on density The adjacent core points of each core point can be determined, and the parking corner points adjacent to each corner point of the parking space can be obtained. Using the characteristics that the adjacent corner points of the parking space are connected by the parking space line, the distorted parking space / special-shaped parking space in the image can be identified, and the parking space can be improved. recognition accuracy. In addition, using each corner point of a parking space as the core point of density clustering can ensure that when the parking space line near the corner point of the parking space is blocked and there are fewer detection points of the parking space line, the parking space corresponding to the corner point of the parking space can also be detected. If it is identified, the parking space inspection rate can be improved and the parking space missed inspection can be avoided.

The invention provides a people counting method and system thereof, belonging to the technical field of image processing. The method includes: performing frame-sequential object detection on a frame-sequenced image set, and when a first image with a first object set is detected in the image set, obtaining an image corresponding to the first object set in the first image The first local vector corresponding to the position of the first image, and then obtain the second local vector corresponding to the position of the second object set relative to the second image in the second image with the second object set in the image set; according to the first local The vector and the second local vector are combined with the preset tracking mapping relationship to obtain a tracking value set, and then according to the relationship between each tracking value in the tracking value set and the preset tracking threshold condition, a tracking state set of objects in the first object set is obtained. The invention realizes the detection and tracking of the high-accurate recall rate and realizes the high-precision and high-robust people flow counting system based on the detection and tracking of the high-accurate recall rate.

The application discloses a method, a device and an electronic device for identifying new words in spam short messages. Wherein, the method includes: obtaining a short message collection; determining a plurality of candidate word sets corresponding to spam short messages; determining the short message category tendency related indicators of the candidate words according to the short message category information; and obtaining the document rarity related indicators of the candidate words; According to the related indicators of SMS category tendency and document rarity, the new word score of spam message is determined; according to the new word score of spam message, the new word of spam message is determined from the candidate word set. With this processing method, most of the new words recalled according to the related indicators of SMS category tendencies are representative of spam messages, avoiding the recall of many normal words; therefore, the recall rate can be effectively improved. At the same time, this processing method makes it possible to recall low-frequency neologisms in spam text messages according to relevant indicators of document rarity; therefore, the recall rate can be effectively improved.