Alam, Md. AshrafulChowdhury, Prabal KumarIslam, Md. AminulHaque, Md Aminul2024-01-102024-01-1020232023-01ID: 22241150ID: 19101398ID: 19101580http://hdl.handle.net/10361/22092Cataloged from PDF version of thesis.Includes bibliographical references (pages 36-38).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.One of the world’s most pressing issues right now is the lack of a competent waste management system, particularly in emerging and underdeveloped countries. Re cycling solid waste, which comprises numerous dangerous non-biodegradable sub stances like glass, metals, plastics, etc., is the most essential step in reducing waste related issues in the environment. Typically, collected waste includes all types of waste that must be thoroughly sorted to recycle efficiently. Most countries use man ual waste sorting techniques, which are efficient. Nevertheless, the waste sorting process by human being is not safe as there is always a risk of exposing them selves to toxic wastes, which could be serious for their health. Our thesis presents a Deep Learning technique based on computer vision for automatically identifying waste. To construct the model, we used Convolutional Neural Networks, real-time object detection systems, such as YOLOv5 and YOLOv7, as well as several trans fer learning-based architectures, including VGG16, MobileNet, Inception-Resnet-v2. The model is trained on numerous images for each type of waste to ensure no overfit ting and greater accuracy. The highest accuracy we achieved for our waste detection model YOLOv5x is 93.7%.38 pagesenBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.TrashNetDeep learningObject detectionImage classificationCNNVGG16Inception-Resnet-v2MobileNetYOLOv5YOLOv7Neural networkImage processingWaste products.Image processing.Thesis