Akademik Veri Yönetim Sistemi
2021 29th Signal Processing and Communications Applications Conference (SIU), 9 - 11 Haziran 2021, ss.1-4
The problem of manually entering the meals one by one, which is one of
the main problems in many traditional diet applications, was solved with
the help of the proposed deep learning models integrated into the
mobile application. Two different models resulting from the deep
learning study were used in the application. The first model detects the
food in the environment with real-time object detection and the second
one recognizes the type of the food in the detected plate. A data set
containing 102 different types of food belonging to Turkish cuisine and
approximately 500 photographs of each type of food was collected and
used in the training of the second model. The proposed TurkishFoodNet
network with the number of three, five, seven, nine, eleven and thirteen
layers were examined in the training of both models. Apart from this
network, training operation were also held with Tensorflow Lite Image
Classifier and MobilNetV2. According to the test results, Tensorflow
Lite Image Classifier and TurkishFoodNet_L11 gives the highest accuracy
for both detecting food and recognizing the type of food with an
accuracy of 93% and 84%, respectively.