基于深度學習的動態(tài)手勢檢測與識別算法研究

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中圖分類號：TP391.4 文獻標識碼：A 文章編號：2096-4706（2025）08-0054-07

Abstract： Gesture recognition is of great significance torealize human-computer interaction.In order torealize highprecisiontarget detection and recognition under dynamicconditions，this paper is based on YOLOv5 target detection firstly and determines the coordinate informationof the target gesture byusing thefeature pyramid structureand multi-scale fusion structuralfeaturesinsidethealgorithm.ThenitusestheMediaPipemodeltodetectthekeypointsofthehand，deterinesthe vectorangleofthehand joints，andanalyzes thefingerbendingsituation，soas tojudge the specific gesture.Using themethods of positiondeterminationand implementationbyusingseparate models foractionclasficationeffectivelyimproves the problem that thereduced recognitionrateof gesturescaused byfactors suchasrotationandoccusionin dynamicconditions.The training samplesare selected fromsixcategories intheopen-source gesturedataset HaGRID.Theexperimentaltestresults demostrate that the mean value of one-hand recognition detection accuracy of the combined algorithm is up to and the detection speed is up to 40 FPS，and the model size is 88.5 MB.

Keywords： gesture recognition; YOLOv5;MediaPipe; hand joint point detection; gesture dataset

0 引言

人機交互是指人與計算機之間通過某種特殊方式實現(xiàn)信息交換的過程，傳統(tǒng)人機交互采用穿戴傳感器的方式，由于傳感器的感知范圍有限且信號不具有普適性的問題，無法滿足人們?nèi)粘Ｉ畹膶嶋H要求。（剩余5938字）