• Varalakshmi P
• Likitha R
• Sujay V
• Imran Khan N
• R A Raajesh

The advent of artificial intelligence technologies has spurred innovations across various domains, and waste management is no exception. This project presents an AI-based garbage detection system designed to revolutionize the identification and classification of waste materials in diverse environments. Leveraging advanced computer vision and machine learning algorithms, the system automates the process of garbage detection and categorization, contributing to more efficient and sustainable waste management practices. Recent advancements in computer vision have opened up new avenues for addressing global concerns surrounding waste management. This study delves into leveraging computer vision techniques for precise waste classification and identification. The primary goal is to develop a robust algorithm capable of accurately recognizing and categorizing various waste containers. Utilizing deep learning algorithms like Convolutional Neural Networks (CNNs), content extraction and classification are performed. The dataset comprises images depicting diverse waste types including plastic, paper, glass, metal, and organic waste.The proposed system involves preprocessing, feature extraction, classification, and postprocessing stages. Image enhancement, normalization, and noise reduction enhance input image quality during preprocessing. Relevant features are extracted using pre-trained CNN models such as ResNet, VGG, or MobileNet. Transfer learning techniques optimize these models for garbage classification tasks.Classification involves training the modified CNN model with labeled data using optimization algorithms like Stochastic Gradient Descent (SGD) and ADAM. Postprocessing techniques like non-maximal suppression (NMS) address production forecasts and eliminate duplicate signals.Experimental results demonstrate the algorithm's effectiveness in accurately classifying and identifying waste types, contributing significantly to waste management efforts. Future research directions include real-time implementation, scalability, and integration with robotic systems for autonomous waste management in industrial and urban settings.

Computer Vision, CNN model, Python, YOLO model, Optimization Alogorithms.

"WASTE CLASSIFICATION AND DETECTION USING COMPUTER VISION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.241-245, May-2024, Available :http://www.jetir.org/papers/JETIRGG06039.pdf

"WASTE CLASSIFICATION AND DETECTION USING COMPUTER VISION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. pp241-245, May-2024, Available at : http://www.jetir.org/papers/JETIRGG06039.pdf