Intelligent Billing system using Object Detection

Abstract

With the rapidly increasing technology and development in machine learning, deep learning and artificial intelligence, improving the billing system is an effective means of reducing wastage of time. Nowadays, even though barcode scanners have become as fast as ever but for fruits and vegetables, it still needs to be entered manually into the computer which is very time taking and hectic process. Vegetable and fruit markets have become an integral part of our life hence in such places the environment must be made hassle free and more importantly, the billing should be less laborious and efficient without wasting time. In order to overcome the existing problems associated with the barcode and RFID tags, we proposed an automatic billing system that detects the fruits and vegetables and then displays the final Bill. The main objective of this project is to detect the fruits, display the fruits detected and then to bill these items. To achieve this, we have used two different algorithms, 1) Fine tuned Convolutional Neural Network that we built from base model. 2) To increase accuracy for real time object detection and for the bounding boxes to be displayed, we used state of the art YOLO based on pytorch as YOLO predicts the bounding boxes and detects the object faster than other detection algorithms and is more reliable.