# Deep-Feature-Based Approach to Marine Debris Classification
## 1 Introduction  
The rising level of marine pollution is a growing environmental problem in today's society. Our oceans, seas, and other water bodies are polluted with various waste items that threaten coastal wildlife, habitat, human safety, and coastal communities' economic health [1]. Discarded fishing gear continues to trap and kill marine life. Animals such as seabirds or turtles often mistake plastic debris with their food due to its similar appearance and odor, which leads to their malnutrition and starvation [2, 3]. In addition, seafood contaminated with microplastics, i.e., small plastic particles less than 5 mm in size, is potentially toxic for humans that consume it [4, 5, 6]. Furthermore, marine debris induces high economic costs in industries such as tourism, aquaculture, and fisheries [7]. The key measures for solving marine pollution include detailed knowledge of magnitudes, sources, and impacts of marine debris, general behavioral change, enhancement of circular economy, prevention of waste items entering the marine environment, reducing waste generation, and removal of debris already present in the marine environment [8]. Identification and the cleanup of marine debris, especially one deep under the water surface, is challenging and expensive. Therefore, to make the cleanup process more efficient, automatized detection and removal of marine debris is desired. The latter can be realized with the aid of autonomous underwater vehicles (AUVs) and deep-learning-based visual identification of underwater waste. Deep learning methods have been successfully utilized on various computer vision tasks, including object detection and localization [9; 10], classification [11; 12], and semantic segmentation [13; 14]. Unlike conventional machine learning techniques, which require domain-specific hand-engineered feature extraction, deep learning techniques automatically learn hidden data representations from the raw data [15] and provide end-to-end learning mechanisms. Convolutional neural networks (CNNs) [16] are one of the leading methods contributing to the success of deep learning when it comes to image and video data. The architectural design of CNNs is inspired by receptive field structures in the animal visual cortex [17; 18] and designed to learn features hierarchically by composing lower-level features into higher-level ones. The three main components of CNN architecture are convolutional, pooling, and fully connected layers. Feature maps are calculated by sliding convolutional kernels across all spatial locations using the predefined stride and computing dot-products between kernel's weights and small local patches in the input volume. Each convolutional kernel produces one corresponding feature map. Lower convolutional layers extract generic features such as lines and edges, while higher layers encode more complex, higher-level features. Nonlinear activation function, usually rectified linear unit (ReLU) [19], is applied element-wise on obtained feature maps to introduce nonlinearity, which is desirable for detecting nonlinear features [20]. Pooling layers reduce the input's spatial size by replacing local patches in input feature maps by their maximum or mean value to achieve invariance to small shifts and distorsions. Some CNN architectures add additional fully connected layers on top of the stacked convolutional and pooling layers before the final softmax layer [21; 22] to perform high-level reasoning [20]. This paper addresses the problem of automatic, image-based marine debris classification and identification by utilizing the features extracted via existing state-of-the-art deep convolutional architectures. In recent years, several works have employed deep-learning-based techniques to tackle the problem of marine debris detection, classification, and quantification. The study in [23] utilizes convolutional neural network architecture to identify marine debris in forward-looking sonar imagery autonomously.