Detection and Classification of Floating Waste on Water Surfaces Using YOLO-Based Algorithms

Boonchoo Jitnupong; Jirawan Charoensuk; Supaporn Bundasak; Supakrit Somritjinda; Nonpawit Silabumrungrad; Jaruwan Suraseing

doi:10.46604/peti.2026.16023

Authors

Boonchoo Jitnupong Department of Computer Science and Information, Faculty of Science at Sriracha, Kasetsart University, Sriracha, Thailand
Jirawan Charoensuk Department of Computer Science and Information, Faculty of Science at Sriracha, Kasetsart University, Sriracha, Thailand
Supaporn Bundasak Department of Computer Science and Information, Faculty of Science at Sriracha, Kasetsart University, Sriracha, Thailand
Supakrit Somritjinda Department of Computer Science and Information, Faculty of Science at Sriracha, Kasetsart University, Sriracha, Thailand
Nonpawit Silabumrungrad Department of Computer Science and Information, Faculty of Science at Sriracha, Kasetsart University, Sriracha, Thailand
Jaruwan Suraseing Department of Computer Science and Information, Faculty of Science at Sriracha, Kasetsart University, Sriracha, Thailand

DOI:

https://doi.org/10.46604/peti.2026.16023

Keywords:

floating waste detection, image processing, object detection, YOLO, environmental monitoring

Abstract

Floating waste on water surfaces is one of the causes in environmental pollution. Foam, plastics, and glass bottles are not readily biodegradable and adversely affect aquatic life. This research focuses on detecting, classifying, and counting non-biodegradable and hard-to-decompose waste floating on water surfaces using object detection techniques. Five classes of waste, cans, foam, plastic bags, plastic bottles, and miscellaneous items, are evaluated in this study. Detection and classification are performed by using six models-YOLOv5, YOLOv7, YOLOv8, YOLOv9, YOLOv10, and YOLOv11-which can identify overlapping objects. This study utilizes two datasets with varying resolutions, as well as two model sizes and two batch sizes. In the experimental evaluation, the YOLOv11 model outperforms the other models with a precision of 83% and mAP50 of 78%. Plastic bags and plastic bottles are classified more accurately by YOLOv11 than by YOLOv5, with improvements of 22% and 15%, respectively.

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