## 1 Introduction  
Ports and surrounding areas of the coast are zones in which a multitude of human activities are concentrated in a limited space with usually low water renewal rates. In consequence, ports and adjacent waters are very sensitive to pollution and accumulation of solid waste and their impact on the aquatic environment and, in turn, socioeconomic impact [1]. A relevant mechanism of water pollution in port areas is waste discharge and accumulation caused by non-continuous discharge events either intentionally or accidentally. This means solid or liquid pollutant waste is discharged into the water instantaneously or during a short period of time. These events constitute one of the most significant aspects to be considered in port and coastal environmental management; thus, economic and robust monitoring techniques are paramount to achieve adequate port water quality [2, 3]. This issue is especially sensitive in city ports where there is a close relation between port operation and city activity, and where city waste and pollution can easily get into port water [4]. Currently, the most common approach for marine pollution monitoring in ports relies on conventional methods of collecting in situ water and waste samples for subsequent analysis in a laboratory. Such methods are time-consuming, expensive and do not provide a real-time picture of water quality in port waters. Thus, in practice they tend to be implemented at minimum levels in order to comply with regulations, especially in ports with scarce resources. The consequences of this limited monitoring at environmental management level are in many cases significant [5; 6]. Additionally, real-time or near real-time measurement and monitoring methods for marine pollutants and waste are necessary for managing their environmental impacts and understanding the processes governing their spatial distribution [7]. These techniques offer a complementary perspective on marine pollution to hydrodynamics-based environmental management techniques [8; 9; 10]. Thus, real-time pollution monitoring techniques can be linked with hydrodynamic models to obtain improved environmental management systems [11]. Given the nature and frequency of these discharges, management systems will usually consider the statistical parameters of the spatial and temporal distribution of the frequency of discharges instead of individual events. Therefore, these systems do not require very high levels of accuracy in monitoring as opposed to critical systems like biomedical applications, but rather enough to offer statistically significant distributions. Monitoring systems that offer 80% or higher accuracy are considered admissible based on the usual values required in these types of applications [12]. In this context, it is important to note that pollution discharge events in ports are, in most cases, visually perceivable. Consequently, it seems feasible to investigate the possibility of establishing automated monitoring systems for these discharges using cameras installed at strategic points in the port. Associated with automatic image analysis systems, computer vision techniques seem an excellent complement according to previous experiences in other fields for detecting and recognition [12; 13; 14]. Computer vision techniques have recently experienced a quick evolution, being implemented in a wide range of different applications with high efficiency and performance [15; 16; 17]. Deep learning on convolutional neural networks is proven to achieve very high performance on computer vision tasks [18]. In fact, remote sensing technology is proven to provide spatially synoptic and near real-time measurements that can be effectively used to detect and manage pollutants such as suspended sediments, oil and chemical spills, algal blooms and high suspended solids [7; 19]. Additionally, recent contributions in waste and pollutant detection used Image Classification based on deep convolutional networks [20; 21]. Such approaches have been successful at addressing pollution detection in large surface areas.