AI-Based Garbage Detection System using YOLOv11
ImageObject Detection
This project develops an AI-based garbage detection system using the YOLOv11 model to identify and classify waste in real time, supporting smarter waste management and environmental sustainability. The system was trained on a dataset of over 4,000 images, which were carefully annotated using Roboflow, where bounding boxes were applied to different types of garbage such as plastic, paper, and metal to ensure accurate learning. By leveraging techniques from Computer Vision and Deep Learning, the model is able to detect waste objects in images or video streams with high accuracy, making it suitable for applications like smart bins, recycling systems, and urban monitoring.
This project develops an AI-based garbage detection system using the YOLOv11 model to identify and classify waste in real time, supporting smarter waste management and environmental sustainability. The system was trained on a dataset of over 4,000 images, which were carefully annotated using Roboflow, where bounding boxes were applied to different types of garbage such as plastic, paper, and metal to ensure accurate learning. By leveraging techniques from Computer Vision and Deep Learning, the model is able to detect waste objects in images or video streams with high accuracy, making it suitable for applications like smart bins, recycling systems, and urban monitoring.
2025 - 2025