Plastic Waste on Water Surfaces Detection Using Convolutional Neural Networks

This paper delves into the use of state-of-the-art convolutional neural network (CNN) architectures for automatically detecting plastic waste on water surfaces.The study extensively examines the efficiency of various CNN models for object detection, specifically targeting the identification of plastic waste.Prior to model training, extensive preprocessing of the dataset was conducted, which comprises imagery representing four distinct categories of plastic litter, namely 'plastic bags,' 'plastic bottles,' 'other plastic waste,' and non-plastic waste.Multiple configurations of YOLO (You Only Look Once) architecture models were trained either from inception or fine-tuned with diverse hyperparameters and varying numbers of epochs.The training process leveraged PyTorch framework and CUDA technology to enhance computational efficiency.Model assessment was conducted utilizing established CNN performance metrics, including precision, mean Average Precision (mAP), recall, and F1 score.The outcomes reveal superior performance of select models or models exhibiting promising results, substantiated by the evaluation metrics employed.Additionally, the study furnishes insights into the strengths and limitations of the trained models, accompanied by recommendations for refinement and avenues for future research.