Working Paper Article Version 1 This version is not peer-reviewed

Reach on Waste Classification and Identification by Transfer Learning and Lightweight Neural Network

Version 1 : Received: 21 February 2020 / Approved: 23 February 2020 / Online: 23 February 2020 (14:01:01 CET)

How to cite: Xu, X.; Qi, X.; Diao, X. Reach on Waste Classification and Identification by Transfer Learning and Lightweight Neural Network. Preprints 2020, 2020020327 Xu, X.; Qi, X.; Diao, X. Reach on Waste Classification and Identification by Transfer Learning and Lightweight Neural Network. Preprints 2020, 2020020327

Abstract

Using machine learning or deep learning to solve the problem of garbage recognition and classification is an important application in computer vision, but due to the incomplete establishment of garbage datasets and the poor performance of complex network models on smart terminal devices, the existing garbage classification models The effect is not good.This paper presents a waste classification and identification method base on transfer learning and lightweight neural network. By migrating the lightweight neural network MobileNetV2 and rebuild it, The reconstructed network is used for feature extraction, and the extracted features are introduced into the SVM to realize the identification of 6 types of garbage. The model was trained and verified by using 2527 pieces of garbage labeled data in the TrashNet dataset, which ultimately resulted in a classification accuracy of 98.4% of the method, which proves that the method can effectively improve the classification accuracy and time and overcome the problem of weak data and less labeling. The over-fitting phenomenon encountered by small data sets in deep learning makes the model robust.

Subject Areas

waste classification; transfer learning; deep learning; recognition classification

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