Deep based learning: ebin sorting system development

Abstract

Vietnam was the fifth country that dumped more plastic into the oceans than the rest of the world combined. The country produces an average of 25.5 million tons of waste per year, of which 75% is buried. Several burial sites in major cities such as Hanoi, Ho Chi Minh city, and Da Nang city are overloaded and negatively affecting citizens’ lives. Therefore, sorting at source would significantly reduce soil resources and groundwater pollution and save money on collection costs, transportation, treatment, and easing the pressure on the landfills. Using computer vision may help classify easily recyclable trash into different categories based on its material. This paper proposes an experiment on a customed Convolution Neural Network (CNN) based waste detector for three classes of trash: Plastic bottles, Cans, and Glass. The study used up to 900 hand-collected image data separated into 500 for analysis and 400 remains for assessing the result. All trashed objects used for generating data were collected around the Ho Chi Minh city University of Technology (HCMUT, Vietnam). The Deep Learning model used in this research, named SSD MobileNet V2, is part of the open-source Tensorflow Object Detection API. The system gave the result, the relative match percentage of different amounts of data we fed the model. The system showed linear relativity between the amount of data trained AI100, AI200, AI300, AI400, AI500, and the mean Average Precision (mAP) when testing the system. From this on, the study conducted another experiment to ensure that the performance of the trained model would meet expectations, AI401. In this experiment, the system showed over 80% inaccuracy overall. By back-analysis, this paper would be a good vision for researchers to study and enhance the system’s accuracy to serve the purpose of trash classification in innovative trash bin applications.