A YOLO Algorithm-based Visitor Detection System for Small Retail Stores using Single Board Computer

Abstract

In Indonesia, assistance for small enterprises has grown in recent years. However, a monitoring system is required to support these enterprises and ensure their expansion and survival. Using a single-board computer and the YOLO algorithm, we construct a visitor tracking system in this study to meet this demand. To capture objects and categorize them as human or non-human, we employ the YOLOv4-tiny model, which has a mAP of 89.21%. Human visitors are welcomed with the use of a speaker. A telegraph bot that notifies the owner of the retail establishment of the visitor's presence also makes the presumption as to whether the visitor is a potential customer or an intruder. Our research demonstrates that the created monitoring system effectively recognizes and categorizes visits, enabling retail store owners to make defensible choices regarding visitor interaction and security precautions. Small business owners can save personnel costs while still maintaining high levels of client engagement and security. The theoretical application of this research is the creation of a visitor monitoring system that is affordable and may be used in small enterprises, particularly in Indonesia. The practical ramifications of our research include the possibility for small retail business owners to boost profits by lowering labor expenses while raising customer satisfaction and security. The importance of our study lies in its role in creating a monitoring system that will support small enterprises and increase their sustainability.