Implementation of Convolutional Neural Network of Non-Biodegradable Garbage Classifier and Segregator Based on VGG16 Architecture
John Vincent Perez, Jovan Avery Dalluay, Gemmalyn Manangan, Diane Katherine Deveza, Rholie Anne Monforte, Camille Mendero, Herbert V. Villaruel · 2023
Problems posed by solid waste management have been raising alarming threats today. The increasing number of garbage clogging the drainage systems and the limited space for waste disposal are some of the vivid indications of the waste crisis. One of the solutions for this problem is an intelligent system for classification and segregation of non-biodegradable wastes is implemented with the aid of Convolutional Neural Networks. The system is trained with an initial dataset coming from the images of the waste categories such as plastic bottles, plastic wrappers, plastic cups, and metal canneries. Through VGG16 deep learning architecture, the system can identify the garbage input and classify them accurately. The significance of this study is regarding automation of the garbage segregation process in building an image classifier model that can be deployed into the Materials Recovery Facilities. This is where they sort and market recyclable wastes for the user-end manufacturers. In this manner, it lessens the production of synthetic materials and magnifies the recycling process. The results are shown through graphical representations of the total accuracy of the system against the images subjected in the testing. Although indirect, this research serves as a solution to present the capability of CNN to solve real-world situations.