Abstract

Aiming at solving the waste sorting problems of smart environmental sanitation, this paper proposes a novel smart waste sorting system, which consists of two sub-systems including a hardware system and a software system. The hardware system is of a trash bin framework based on the core module Raspberry Pi and the software one is of an image classification algorithm platform based on SURF-BoW algorithm and multi-class SVM classifier. In our experiment, the images produced during training and testing are both obtained from webcam in our system and extra processing with affine transformation and noise-adding operation. The experimental results show that among the five categories of waste, the battery waste performs best with 100% classification accuracy. Besides, the average classification accuracy is up to 83.38%. Therefore, our system has reliable practicability and robustness, which is expected to be applied to deal with the waste sorting problems in our daily life.

This work is licensed under a Creative Commons Attribution 4.0 License.