## 2 Materials and Methods
### Computer Vision Technique and Application to Pollution in Port Waters  
Computer vision is a field where applications are developed using convolutional neural networks that are trained using deep learning techniques. Specifically, it can be defined as a set of techniques to automatically obtain descriptions or significant parameters from the images of physical objects; these descriptions can be useful for decision making. This is the case of the current investigation included in the field of marine waste and litter detection. Due to the numerous potential applications of Computer vision, it has experienced an important development in the recent years.  
An artificial neural network is a collection of connected nodes which loosely model the neurons in a biological brain [26]. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. When an artificial neuron receives a signal, it processes it and, as a result, sends outputs (real numbers) to neurons connected to it. In turn, the output signals of each neuron are computed by some non-linear function of the sum of its inputs. Typically, neurons are aggregated into layers; different layers may perform different transformations on their inputs coming from the one before. Signals travel from the first layer (or input layer) to the last one (or output layer). Figure 2 depicts schematically how neurons in different layers interact to provide meaningful results.  
A Convolutional Neural Network (CNN) is a type of artificial neural network most commonly applied to analyze visual imagery because they are shift invariant (or space invariant), meaning that the position of a feature in an image is not important. This is due to the CNN having a shared-weight architecture of the convolution kernels or filters that slide along input features and provide translation equivariant responses known as feature maps.  
Figure 1: Study area location. Zones where images were obtained are highlighted in yellow in the lower panel.  
Figure 3 shows how the CNN architecture works towards generating relevant information from an input image.  
The most important computer vision techniques are Image Classification, Object detection, Object tracking, Semantic segmentation and Semantic instance segmentation. Although all these techniques have a potential application in port water quality monitoring, the most appropriate technique according to the input-output information desired is Image Classification. This is due to the fact that the amount of information needed to train the system is lower compared to other techniques, and it allows the classification of images into simple classes that can be used to build temporal and spatial distributions of pollution events [27].  
Thus, the aim of this investigation is to evaluate the efficiency of training Image Classification algorithms that, when taking as input the images of port water provided by a camera monitoring system and operating in real-time, provide as output the class to which each image belongs with the highest probability, according to a classification that is relevant for proper environmental management of port water.