Deep Learning-Based Automated Cashier System for Bakeries

The application of image recognition in the bakery business has paved the way for automatic payment systems, a significant advancement in the field of computer vision. This article delves into an exploration of advanced image recognition models to meticulously assess their effectiveness, speed, and suitability for seamless integration into specialized automatic payment systems tailored for bakeries. Specifically, YOLOX, YOLOv8, Faster R-CNN, and RetinaNet, each with different versions and backbones, are considered for evaluation based on their speed and performance. Notably, this study introduces a streamlined process for rapidly creating custom datasets for object detection research and evaluates models across these datasets. The insights and analyses derived from this study provide valuable perspectives for optimizing processes and enhancing the overall performance of automatic payment systems within bakeries.