Object Detection in Shelf Images with YOLO

Abstract

Object detection in shelf images can solve many problems in retails sales such as monitoring the number of products on the shelves, completing the missing products and matching the planogram continuously. This study aims to detect object in shelf images with deep learning algorithms. Firstly, object detection algorithms and datasets are examined in the literature. Then, experimental study is performed using Coca Cola images obtained from Imagenet and Grocery dataset with YOLO (You Only Look Once) algorithm. Results of the study are discussed from different sides such as number of classes, threshold values and numder of iteration.