复杂场景中准确实时的人物识别算法研究OA

Currently,single step object detectors based on Deep Learning have been widely used for real-time object detection,but their positioning accuracy for targets is poor,and there are problems such as missed detection and false detection of targets.This paper proposes an accurate and real-time character recognition algorithm for complex scenes.Firstly,this paper uses Gaussian YOLOv3 to estimate the coordinates and positioning uncertainty of the prediction box.Then,a Non-Maximum Suppression method based on Attention Mechanism is used to remove redundant detection boxes and improve the accuracy of target detection results.After self-built dataset training and testing,the improved Gaussian YOLOv3 has a character recognition accuracy of 83.1%,which is 1.68%higher than YOLOv3.The detection model can be applied to the recognition and positioning of military battlefield characters,providing effective technical support for battlefield situation awareness systems.