Classification of Recyclable Waste Using Deep Learning: A Comparison of Yolo Models

This study aimed to enhance the effectiveness of waste management systems to reduce harm to the environment and promote sustainability.The main objective of this study was to assess the capability of YOLOv5 and YOLOv8 to detect potential recyclable waste materials.YOLOv5 and YOLOv8 deep learning architectures, primarily focused on modern features, recognize and handle recyclable waste materials.The study application was based on the waste recycling plant dataset (WaRP), which was created to facilitate this platform's modification for garbage classification machine learning.YOLOv5 has been remarkably successful at waste detection because it uses a multi-scale detection system.The most memorable achievement of this study is the radical improvement in its detection accuracy and speed of performing the detection task seen in YOLOv8.YOLOv8 is a super-ordinate model that outperforms its predecessors' detection speed and resolution.The baud rate of the various object classes is unequal across object classes.Detection accuracy must be higher, especially for categories with below-average output performance.Precision and recall values for YOLOv5 and YOLOv8 are 0.478 and 0.569 and 0.442 and 0.513, respectively.Approximately 40.6% and 54.6% of the mAP50-IOU and 42.6% values outperform the range offered by YOLOv5.The improved performance of YOLOv8 compared to YOLOv5 demonstrates this platform's potential to enable a more accurate and timely waste management system while conserving the environment.