Deep Sort: AI-Driven Garbage Segmentation & Categorizations

This study introduces an advanced deep learning system for efficient e-waste management, leveraging Convolutional Neural Networks (CNNs) to tackle the escalating challenges posed by increasing urban waste. With rapid urbanization contributing to higher volumes of e-waste, traditional waste management systems struggle to cope, leading to environmental degradation and resource inefficiencies. The proposed system utilizes CNNs for accurate image analysis and classification, achieving an exceptional accuracy of 96%. Developed using Python alongside frameworks like Keras and TensorFlow, the model efficiently classifies e-waste and supports decision-making through an intuitive Graphical User Interface (GUI). Rigorous testing confirms the system's high performance, demonstrating its capability to transform waste management practices. This research offers a scalable and intelligent approach, fostering sustainability and contributing to cleaner, greener urban environments.