## 4 Discussion  
### Results Discussion System Set-Up  
Results showed in Section 3 demonstrate that Image Classification is adequate for marine pollution monitoring tasks due to its high _Accuracy_ rates and low training requirements (Table 2). The system obtained a 91% accuracy rating, which can be considered a sufficient value for the requirements of a discharge management system in which the use of pollution event data is statistical and, if action is required, it will entail necessary direct validation. The time required by the trained algorithm to classify an image is approximately one second, which is compatible with the needs of real-time monitoring. The best performance was proven to be achieved when image set sizes for all classes are similar (Figure 5),  
Figure 7: (**a**) Dots: Accuracy vs. Number of Images per class. Line: Regression. (**b**) Standard deviation of accuracy measures vs. Number of Images per class (when two numbers are displayed it means a number range).  
providing the first insight into the requirements for adequate system implementation. In practice, spill images are difficult to obtain in great numbers and commonly clean water images will be the most dominant class. Thus, in order to achieve an algorithm that holds optimal performance, spill and waste images have to be obtained to achieve a total number between the three classes which is higher than 297 (Figure 6). In this sense, the results of experiments 2 and 3 are consistent with other documented application cases based on computer vision (e.g., [27]).  
The most appropriate performance metric to evaluate these systems in operation is the proposed _Error index_, since in operating conditions it is foreseeable to find a much higher number of class 0 images than those of classes 1 and 2.  
Our work suggests that the most appropriate way for the monitoring system to be implemented is through progressive implementation. In this sense, datasets would be ever increasing when additional spill and waste images were attained. At these points, the algorithm would be retrained with new datasets in order to generate higher _Accuracy_ and lower Error Index rates, improving the information provided by the system in a gradual manner. After a total image data set of 297 (between the three classes considered) has been reached, retraining would be less frequent due to the fact that performance only increases gradually after that point. In this type of progressive implementation, functional monitoring systems would be provided to port decision makers in a shorter time frame while also reducing the total development cost for a system with the same accuracy level.  
Considering that the training time of the algorithm is in the order of minutes and that retraining of the algorithm will be carried out very infrequently (due to the difficulty of obtaining pollution images), it is preferable that each training is carried out from the initial model and not from the previously used algorithm in order to prevent error propagation.  
Figure 8 depicts how the proposed implementation would be carried out in practice. In addition to the image acquisition and identification of the three classes (with alarms generated when spill or waste were detected), with increasing waste and spill images a verification and dataset enhancement step would be prompted in the system. With the enhanced dataset, the algorithm would be revised in order to achieve gradually better _Accuracy_ and _Error index_ performance.