Automatic detection and quantification of floating marine macro-litter in aerial images: introducing a novel deep learning approach connected to a web application in R
| Autor: | Odei Garcia-Garin, Toni Monleón-Getino, Alex Aguilar, Luis Cardona, Morgana Vighi, Pere López-Brosa, Asunción Borrell, Ricardo Borja-Robalino |
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| Přispěvatelé: | Universitat Politècnica de Catalunya. Departament de Matemàtiques, Universitat Politècnica de Catalunya. Doctorat en Estadística i Investigació Operativa |
| Rok vydání: | 2021 |
| Předmět: |
Marine litter
Teledetecció Informàtica::Automàtica i control [Àrees temàtiques de la UPC] 010504 meteorology & atmospheric sciences Emerging technologies Computer science Health Toxicology and Mutagenesis Convolutional neural network 010501 environmental sciences Toxicology computer.software_genre Unmanned aerial vehicles 01 natural sciences Xarxes neuronals convolucionals Aprenentatge automàtic Machine learning Web application Marine ecosystem Macro 0105 earth and related environmental sciences Residus Contextual image classification business.industry Deep learning General Medicine Remote sensing Waste products Pollution Automation Control automàtic Mar -- Residus Convolutional neural networks Artificial intelligence Data mining business computer |
| Zdroj: | Dipòsit Digital de la UB Universidad de Barcelona UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) |
| Popis: | The threats posed by floating marine macro-litter (FMML) of anthropogenic origin to the marine fauna, and marine ecosystems in general, are universally recognized. Dedicated monitoring programmes and mitigation measures are in place to address this issue worldwide, with the increasing support of new technologies and the automation of analytical processes. In the current study, we developed algorithms capable of detecting and quantifying FMML in aerial images, and a web-oriented application that allows users to identify FMML within images of the sea surface. The proposed algorithm is based on a deep learning approach that uses convolutional neural networks (CNNs) capable of learning from unstructured or unlabelled data. The CNN-based deep learning model was trained and tested using 3723 aerial images (50% containing FMML, 50% without FMML) taken by drones and aircraft over the waters of the NW Mediterranean Sea. The accuracies of image classification (performed using all the images for training and testing the model) and cross-validation (performed using 90% of images for training and 10% for testing) were 0.85 and 0.81, respectively. The Shiny package of R was then used to develop a user-friendly application to identify and quantify FMML within the aerial images. The implementation of this, and similar algorithms, allows streamlining substantially the detection and quantification of FMML, providing support to the monitoring and assessment of this environmental threat. However, the automated monitoring of FMML in the open sea still represents a technological challenge, and further research is needed to improve the accuracy of current algorithms. |
| Databáze: | OpenAIRE |
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