Quantum Checkout: An Improved Smart Cashier-less Store Checkout Counter System with Object Recognition

Object detection is part of computer vision technology which is classified as part of Artificial Inelegance, This paper describes the design, development and testing of Smart Cashier-less checkout counter for retail stores, using realtime object detection the technology is used by the system to facilitate the process of check out smartly and without the need for a cashier. This system is the first part of “Quantum”, a smart integrated system that provides an improved shopping experience. The concept of Smart Cashier Desktop Application is to create an application for the store retailers to monitor their products in real-time and focused on the object detection. The second part is a supporting mobile application. The new approach is proposed in this paper, which is not only fast but also effective. Using item identification method, the system detects any object in the image with outlined rectangular boxes and classify each object in the image and place its tag with these methods and algorithms through the deep learning machines. The object was identified through the process of training of datasets. Thousands of images for each object was taken to improve the accuracy. Then, the object in the image was outlined and labelled to be identified during real-time detection. Upon completion of training of datasets, each object can be identified with the proper labelling. Hence, more research and improvement are done on object detection to create a better algorithm.