Fruits Recognition using Deep Convolutional Neural Network for Low Computing Device

Abstract

Artificial intelligence is one of the most developed fields in Computer Science where a lot of researches had been done to make the computer smarter to perform human-like task. One of the most common human-life task research that had been done is object recognition. Convolutional Neural Network is one of the most popular deep learning model to perform a good object recognition. While improving CNN model can be done by simply increasing the depth of its architecture, some researchers prove that as the CNN architecture go deeper, the accuracy will get worse. ResNet, with their residual layer, successfully lift the limitation, but ResNet by itself is too heavy for a mobile or low computing device. This paper proposes a new model which could reach the accuracy of ResNet while having faster prediction time. The proposed model and other state-of-the-art models had been trained on our own fruits and vegetables dataset. The result shows that the proposed model can reach the same accuracy as Resnet110 and overcome the accuracy of DenseNet121 while being faster than those models.