A Learning Framework for Target Detection and Human Face Recognition in Real Time

Inspired by the function, mechanism and efficiency of the visual nerve system of human beings, a revolutionary detection and reorganization method named YOLO is present to provide an accurate, stable and fast arithmetic for a variety of targets, be it target detection for unmanned vehicle, car license recognition and optimization for surveillance. The traditional method for object detection is to reuse the classifier to implement detection, in contrast, the method named YOLOV2 process this problem by considering it in the mathematical area as a regression of spatially discrete bordered areas and relative class probability. However, as a cost of stable and fast response of this arithmetic, inaccurate detection maybe caused by YOLOV2 when the detected object is tiny (e.g., face recognition in surveillance). In this article, the authors provide a new method to further improve the performance of YOLOV2 by utilizing the accurate, stable and fast properties of YOLOV2 and editing the original code of YOLOV2 to eliminate the inaccuracy of tiny object detection, and implement this method on an embedded system.