Melek, Ceren GulraSonmez, Elena BattiniAlbayrak, Songul2024-07-182024-07-182019978-1-5386-9301-8https://doi.org/10.1109/eurocon.2019.8861817https://hdl.handle.net/11411/777618th IEEE International Conference on Smart Technologies (IEEE EUROCON) -- JUL 01-04, 2019 -- Novi Sad, SERBIAObject 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.eninfo:eu-repo/semantics/closedAccessDeep LearningObject DetectionProduct RecognitionYoloConference Object10.1109/eurocon.2019.8861817N/AWOS:000556109600070