| dc.description.abstract | Sometimes there are too many customers, and manual weighing will inevitably encounter more customers. The clerk could not assist in weighing in time and waste a lot of time for the customer. Therefore, this thesis aims to establish a pricing system based on AI technology for recognizing vegetables, fruits, and breads. When the customer puts the purchased items on the electronic scale, the system will automatically recognize whether the items are vegetables, fruits, or pieces of bread. Then total price calculates according to the weight or the quantity. Customers follow the checkout website until all purchases are weight, then a shopping list is printed. This can greatly reduce the manpower requirements of the store and save the time customers waiting for one-to-one staff assistance.
Literature [1] recognized 40 kinds of fruits and vegetables placed in colorful plastic bags. This thesis improves on this basis by adding bread and green leafy vegetables. Considering the increase in recognizing items, this thesis divides the items into three categories: fruits and vegetables, leafy vegetables and breads. The YOLOv5m6 network architecture is used to detect and preliminarily recognize the vegetables, fruits, and breads, and calculate the number of items at the same time. The EfficientNet-B6 network architecture was used to recognize 10 kinds of leafy vegetables and 25 kinds of bread, respectively, and weighted majority voting was used for fruit and vegetable recognition [1]. Considering that when a large number of breads are purchased, the breads on the plate may be closely placed or stacked. This thesis designs a masking mechanism to improve the accuracy of bread recognition. This mechanism can reduce the influence of bread stacking, and improve the accuracy of bread recognition. The system can recognize a total of 40 kinds of vegetables and fruits, 10 kinds of leafy vegetables and 25 kinds of bread. All the testing data are taken under the white light. The accuracy rate of vegetable and fruit recognition is 96.9% [1], the accuracy of leafy vegetables recognition is 98.6%, and the accuracy of bread recognition is 99.5%. This thesis improves the checkout website [1]. A new bread weighing interface will be added to the webpage, and the description text will be added to the interface to guide users through the weighing process. | en_US |