This paper details the design and implementation of a garbage detection system utilizing YOLOv5, a cutting-edge, real-time object detection neural network. The system employs the pre-trained YOLOv5 Nano model, which has been adapted to a dataset specifically tailored for garbage detection. A significant challenge encountered during the study was the issue of model convergence, attributed to data inconsistencies in determining what constitutes garbage. To address this, a series of custom datasets were utilized in a trial-and-error approach, enabling the model to attain stability and achieve reliable performance metrics. The study further incorporates an MLOps pipeline, streamlining the processes of data collection, processing, model training, and testing. This pipeline optimizes the garbage detection model for deployment on edge devices, enabling cameras to capture video stream metadata for more efficient trash collection. By integrating AI-driven object detection with video metadata, the system promotes environmental sustainability through improved waste management practices.