# Deep-Feature-Based Approach to Marine Debris Classification
## 6 Conclusions  
Marine debris poses a major threat to the marine ecosystem and negatively affects today's society in an environmental, social, and economical way. Motivated by the need for automatic and cost-effective approaches for marine debris monitoring and removal, we employ machine learning techniques together with deep learning-based feature extraction to identify and classify marine debris in a realistic underwater environment. This paper provides a comparative analysis of common deep convolutional architectures used as feature extractors for underwater image classification. Furthermore, it explores the best ways to use deep feature extractors by analyzing three different modes for utilizing pre-trained deep feature extractors and examining the performance of different ML-based classifiers trained on top of extracted features.  
The fine-tuning of the pre-trained feature extractor network's weights with appropriate learning rates during the whole training procedure showed the most prominent results in our experimental setup. The best performance is shown by Inception-based FT feature extractors, namely Inception-ResNetV2 and InceptionV3, achieving an overall accuracy of more than 90%, more precisely 91.40%, and 90.57%, respectively, when trained with the NN classifier on top. Traditional SVM and LR classifiers exhibited as credible alternatives to the NN classifier, which often outperform the NN classifier. The SVM trained on Inception-ResNetV2 features achieves 91.61% accuracy, while the LR classifier trained on the InceptionV3 features obtains accuracy of 90.78%. Considering the inherent challenges that come with automatic marine debris classification in underwater imagery, the obtained results demonstrate the potential for further exploitation of deep-learning-based models for real-time marine debris identification and classification in natural aquatic environments.  
In the future, we hope to assemble our dataset containing images of marine debris in Croatian Adriatic Sea underwaters to utilize a deep-learning approach for automatic marine debris identification in the local marine environment. The main focus of this paper is the problem of marine debris classification. Future research should focus on expanding the conducted analysis to detect waste objects under the sea level: comparing different object detection architectures with different backbone convolutional network architectures. Furthermore, this work discusses only three approaches to transfer learning: (1) keeping the pre-trained feature extractor network frozen; (2) fine-tuning its weights during the whole training procedure; (3) freezing the feature extractor during the first phase of training and afterward unfreezing it to fine-tune its weights during the second training phase. In future research, it would be interesting to extend the analysis to the case where the first layers of the pre-trained feature extractor network remain fixed while the rest of the network corresponding to more domain-specific features become fine-tuned. Moreover, the optimal way to split the feature extractor into a frozen and fine-tuned part can be further analyzed for each network architecture.  
Conceptualization, S.M. and S.G.; methodology, I.M. and S.M; software, I.M. and G.Z.; validation, I.M. and S.G.; formal analysis, I.M.; investigation, S.M.; resources, G.Z.; data curation, I.M. and G.Z.; writing--original draft preparation, I.M. and S.M.; writing--review and editing, I.M. and S.M.; visualization, I.M. and G.Z.; supervision, S.M. and S.G.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The image and numerical data used to support the findings of this study are available from the corresponding author upon request as the data also form part of an ongoing study.