Real-Time Waste Object Segregation Using Convolutional Neural Network

The increasing number in solid waste in the urban area is becoming a great concern, and it would result in environmental pollution and may be dangerous to human health if it is not properly organized It is important to have an advanced or intelligent waste management system to manage a variety of waste material. One of the most important steps of waste management is the separation of the waste into the different components and this process is normally done by handpicking. To simplify the process, we propose an intelligent waste material classification system, which contains 14 million images belonging to 1000 classes (VGG16) Convolutional Neural Network model which is used to pre-train dataset and serves as the extractor, and Support Vector Machine (SVM) which is used to classify the waste into different groups or types such as glass, metal, paper, and plastic etc. The proposed system is examined on the trash image dataset which was developed by Karen Simonyan and Andrew Zisserman, and is able to achieve an accuracy of 92.7% on the dataset. The separation process of the waste will be faster and intelligent using the proposed waste material classification system without or reducing human involvement.