Parcel Classification and Positioning of Intelligent Parcel Storage System Based on YOLOv5

Abstract

Parcel storage provides last-mile delivery services as part of the logistics process. In order to build an intelligent system for parcel storage, we conducted a study on parcel box recognition using AI’s deep learning technology. Box detection and location estimation studies were conducted using the YOLOv5 model for parcel recognition, and this model is presently being applied to many studies because it has excellent object recognition and is faster than previous models. The YOLOv5 model is classified into small, medium, large, and xlarge according to the size and performance of the model. In this study, these four models were compared and analyzed to perform an experiment showing the optimal parcel box recognition performance. As a result of the experiment, it was determined that the precision, recall, and F1 of the YOLOv5large model were 0.966, 0.899, and 0.932, respectively, showing a stronger performance than the other models. Additionally, the size of the YOLOv5large is half that of YOLOv5xlarge, and the YOLOv5large showed the optimal performance in object recognition of the parcel box. Therefore, it seems that the basis for building an intelligent parcel storage system, which shows optimal efficiency in real time using the YOLOv5large model, can be laid through the parcel object recognition experiment conducted in this study.