| dc.description.abstract | The main purpose of this thesis is to develop a self-service electronic weighing system for recognizing and pricing fruits and vegetables automatically. The object detection neural network and image classification neural network with deep learning are combined to build the system in order to achieve the desired goal. When customers put the fruits and vegetables, no matter inside plastic bags or not, on the electronic scale, their categories can be recognized quickly and accurately as well. The unit price, weight and the total price of fruits or vegetables will be shown on the designed screen and the total price will be calculated. Then customers can print the bill. In this way, the electronic scale can reduce the customer’s waiting time for weighting and save the labor cost for the store, so that the purpose of saving time, saving money and self-service can be reached simultaneously.
This system uses Raspberry Pi 4 as a mobile device to monitor the weight change of the electronic scale. A Raspberry Pi 4 can support multiple electronic scales. The device takes a photo and sends it to the computer when the weight is fixed and not zero. Firstly, the computer uses an object detection neural network Efficient Det to filter out background and plastic bags based on the location information and crop several clean images. Then, we use four neural network models Efficient Net, ResNet, Mobile Net and Denese Net, respectively, to recognize the category of the fruits and vegetables. At last, we make the majority voting for the results from four models to get the final category result. Furthermore, the system adopts parallel and multi-threading to speed up the recognition process. On the other hand, to improve the recognition accuracy, the Focal Loss Function and SAM (Sharpness-Aware Minimization) are used to improve the system performance. The Focal Loss Function is used to solve the imbalance of the training data set and SAM is used to improve the robustness of the system. Based on performing lots of experiments, the proposed system can recognize 40 kinds of fruits and vegetables with 96.9% recognition accuracy. | en_US |