# Deep-Feature-Based Approach to Marine Debris Classification
## 1 Introduction
In [24], a single-stage RetinaNet detector trained on non-aquatic waste images is used to detect trash items in aquatic surroundings. [PERSON] et al. use VGG16 convolutional model architecture to automatically classify floating macro-plastic marine debris images in three categories (bottle, bucket, and straw) [25] and distinguish between six types of plastic debris, one type of marine life, and other items encountered at the shoreline or the seawater [26]. [PERSON] et al. [27] evaluate four different neural network architectures for visual detection of plastic debris in realistic underwater environments. Models were trained to detect three classes of objects in underwater videos: plastic, all man-made objects intentionally placed in the environment, and all biological material. [PERSON] et al. [28] use region-based CNN for the automated detection of seafloor marine litter on imaginary acquired in Ermoupolis bay, Syros Island, Greece. [PERSON] et al. [29] carried an initial study out on the performance of neural networks for the detection and classification of underwater sea litter images trained and tested on a dataset built using images available from the Internet and hybrid images generated using a Blender environment from given background images and 3D litter models. In [30], machine learning techniques, including CNNs, are utilized to automatically classify images of five types of microplastic particles present on beaches in the Canary Islands. The quantification of marine debris on beaches by using unmanned aerial vehicles and deep learning techniques is discussed in [31; 32]. Automatized identification of marine debris comes with many challenges. First, there are various types of marine debris, with significant in-class variations. For example, plastic bottles, bags, and cups have different shapes but belong to the same type of marine debris-plastics. Second, waste objects should be detected in various degradation phases, no matter the recording angle, water turbidity, or illumination. Moreover, training deep neural networks requires a large amount of annotated data. Acquisition of a sufficiently large and diverse dataset for image-based marine debris classification is expensive and not always feasible. A common and effective strategy to overcome a deficit of labeled training data is transfer learning, i.e., knowledge transfer from source to the desired target domain [33]. The idea is to reuse features learned by the network pre-trained on a very large dataset, such as the ImageNet [34] dataset, which contains 1.2 million training images from 1000 classes, on a new task of interest. There are two common approaches to transfer learning: (1) using a pre-trained network as a fixed feature extractor [35; 36]; (2) fine-tuning a pre-trained network's weights on a target dataset [37; 38]. In the first approach, the last, usually fully connected, part of the network is removed, while the remaining part (convolutional base) stays fixed during the training. On top of the fixed base used for feature extraction, a new classifier, either a new neural network or a conventional machine learning classifier such as a support vector machine or a logistic regression classifier, is added to learn underlying patterns from extracted features and discriminate data according to them. The fine-tuning approach uses the pre-trained network weights as a weight initialization scheme. During the training, these weights are updated by backpropagating errors from the target task into the base network to improve the final generalization performance on a new task of interest [37]. The successful employment of transfer learning has been reported on computer vision tasks from various domains, such as medical image analysis [39; 40; 41; 42], agriculture [43; 44], remote sensing [45; 46], and the textile industry [47]. Transfer learning has been utilized in several works considering marine debris identification and classification.