RecycleVision AI: Automated Waste Classification using Deep Learning and Explainable AI

Sankarapu Reddy Mahesh, S. Manjunath Reddy

Waste Classification; Deep Learning; MobileNetV2; Computer Vision; Explainable AI; Grad-CAM.

Waste management has emerged as a critical environmental concern due to rapid urbanization and increasing consumption patterns. Traditional waste segregation methods rely heavily on manual processes, which are inefficient, error-prone, and hazardous to human health. To address these challenges, this paper proposes RecycleVision AI, an intelligent waste classification system based on deep learning and computer vision techniques. The system utilizes the MobileNetV2 architecture with transfer learning to classify waste images into six categories: plastic, paper, glass, metal, cardboard, and general trash. A key contribution of this work is the integration of Explainable Artificial Intelligence using Grad-CAM, which provides visual insights into the model’s decision-making process by highlighting important image regions. The system is implemented using TensorFlow and deployed via a Streamlit-based web interface, enabling users to upload images and receive real-time classification results along with confidence scores and visual explanations. The proposed solution demonstrates high accuracy, computational efficiency, and usability, making it suitable for real-world waste management applications. It contributes to sustainable development by improving waste segregation efficiency and promoting responsible recycling practices.

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