Automated Trash Detection and Classification Using CNN: A Deep Learning Approach

A research project focuses on creating automated trash detection and classification through convolutional neural networks (CNNs) with an objective to improve waste management systems. The waste classification system organizational strategy sorts of waste materials through plastic, metal, glass, paper and organic waste categories. This project implements a data processing framework that includes data preparation followed by augmentation techniques for CNN model design and detailed evaluation protocols. The developed system reached a training accuracy of 90.07 % accompanied by validation accuracy of 78.10 %. Precision rates supported the results at 87 % while recall scores reached 78 % and F1 scores achieved 80 %. The system demonstrates effective reliability based on the test results obtained. The study provides environmental sustainability benefits through accurate waste segregation while cutting down human mistakes and advocating for recycling efforts. The developed system demonstrates scalability together with adaptability that makes it suitable for implementation in urban areas and industrial sites and recycling centers. Additionally forthcoming research activities will target the performance and operational capacity to extend usage of the model for real-world waste management operations.