Fast Detection of Objects Using a YOLOv3 Network for a Vending Machine

Abstract

Fast object detection is important to enable a vision-based automated vending machine. This paper proposes a new scheme to enhance the operation speed of YOLOv3 by removing the computation for the region of non-interest. In order to avoid the accuracy drop by a removal of computation, characteristics of a convolutional layer and a YOLO layer are investigated, and a new processing method is proposed from experimental results. As a result, the operation speed is increased in proportion to the size of the region of non-interest. Experimental results show that the speed is improved by 3.29 times while the accuracy degradation is 2.81% in mAP-50.