Fruit classification model based on residual filtering network for smart community robot

Abstract

With the rapid development of computer vision and robot technology, smart community robots based on artificial intelligence technology have been widely used in smart cities. Considering the process of feature extraction in fruit classification is very complicated. And manual feature extraction has low reliability and high randomness. Therefore, a method of residual filtering network (RFN) and support vector machine (SVM) for fruit classification is proposed in this paper. The classification of fruits includes two stages. In the first stage, RFN is used to extract features. The network consists of Gabor filter and residual block. In the second stage, SVM is used to classify fruit features extracted by RFN. In addition, a performance estimate for the training process carried out by the K-fold cross-validation method. The performance of this method is assessed with the accuracy, recall, F1 score, and precision. The accuracy of this method on the Fruits-360 dataset is 99.955%. The experimental results and comparative analyses with similar methods testify the efficacy of the proposed method over existing systems on fruit classification.