A CNN Plastic Detection Model for Embedded Platform of ROV

Abstract

Plastic pollution has a negative influence on biodiversity especially in aquatic ecosystems, and it has been labelled as one of the greatest dangers to biota. This paper proposes a Convolutional Neural Networks (CNN) based plastic detection model for the embedded platform to identify different shapes of underwater plastics such as bags, bottles, containers, cups, nets, pipes, ropes, snack wrappers and tarps. The model is optimized for Raspberry Pi using OpenVINO framework, with the intention to produce a cost-effective edge system for a Remote Operating Vehicle (ROV) system. The development of the model utilizes a pre-trained object detection model from YOLOv5 and the TrashCan 1.0 dataset, for training and testing. The final model exhibits a good performance, achieving more than 85% accuracy in the overall prediction, which highlights the model’s accuracy and reliability in detecting and classifying underwater plastic shapes. Results from this work highlight the potential of the deep learning (DL) real-time embedded processing at the edge rather by a separate computer on land, using a cost-effective embedded platform.