Construction of a Real-Time Detection for Floating Plastics in a Stream Using Video Cameras and Deep Learning.

ABSTRACT: Rivers act as natural conduits for the transport of plastic debris from terrestrial sources to marine environments. Accurately quantifying plastic debris in surface waters is essential for comprehensive environmental impact assessments. However, research on the detection of plastic debris in surface waters remains limited, particularly regarding real-time monitoring in natural environments following heavy rainfall events. This study aims to develop a real-time visual recognition model for floating plastic debris detection using deep learning with multi-class classification. A YOLOv8 algorithm was trained using field video data to automatically detect and count four types of floating plastic debris such as common plastics, plastic bottles, plastic film and vinyl, and fragmented plastics. Am

SUBMITTER: Lee H 

PROVIDER: S-EPMC11991434 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature