## 7 Conclusions  
HSFW can already be used for practical purposes of drone monitoring according to results previously established. However, the structure of HSFW is modifiable and may include several frameworks to improve monitoring. The functions of sub-frameworks realized in this project allow oil pollution monitoring at sea by drones. Significantly, artificial intelligence and hardware development as separate interacting sub-frameworks may be applied for any type of robot. Testing sub-frameworks in real conditions showed the difficulties that arise during a monitoring mission. The test results determined the directions for further improvement of  
Figure 12: Demonstration of AI HSFW2 element for recognition of marine fuel type by LIF spectrum. the HSFW. First of all, problems were caused by an insufficient number of video images of slicks of various types of oil products for training. The HSFW1 slick detection element exhibited less sensitivity to the type of marine fuel. The main problem in its use was the strong dependence of the spot detection probability on the observation angle. However, the segmentation and classification element of HSFW1 was more dependent on the types of oil products. In addition, there was a stronger dependence on the presence of sun glare and the position of the camera relative to the sun. We used the simplest color-coded spot separation principles based on the analysis of RGB pixel values. The number of images of various types of thin slicks and the images obtained under different lighting conditions in the data sequence on which the training was performed should be raised to increase the reliability of the classification results. A special archive of slick images, where it is possible to upload and download images of real oil slicks for detection, segmentation, and classification of slicks using deep learning, is necessary since images of real slicks are needed. The results of the identification of types of marine fuel solutions by the HSFW2 element were less dependent on the conditions under which monitoring was performed. There were a relatively large number of spectra for solutions representing the most probable types of fuel that can cause pollution. Reference spectra were obtained for various concentrations. Thus, at present, this dataset may be used as it is for reliable identification. The drone body significantly shields solar radiation; thus, identification showed good results in daytime conditions when the LIF spectra from solutions were measured. The method for identifying fuel types in a slick based on LIF spectra should be improved. The dataset of spectra from slicks of various types should also be expanded. Furthermore, the training methodology for recognizing spectra from slicks and tools to increase ratio values of the LIF signal to sun background can be improved. The latter is possible if short laser pulses and a time gate are used for LIF registration. However, the experiment at the laboratory pool showed that it is possible to identify types of fuel in the slick if there is no direct sun radiation (cloudy weather, in the evening when the contribution of solar radiation in the spectral range of 320-450 nm does not exceed the fluorescence intensity from the slicks). In this study, the individual HSFW3 elements most important for autonomous navigation and motion control were developed and tested. However, testing these elements, as well as testing the interaction of individual elements of the sub-frameworks (including the Priority HSFW element) demonstrated the correctness of the chosen framework approach. In the future, a demanded element is one that provides autonomous navigation of the drone in the absence of GNSS signals. The element should provide autonomous navigation through the video image of an object in the monitoring area (localization and mapping element of the system). The simplest and most affordable option for standalone monitoring is the use of HSFW3 in conjunction with only the HSFW1 slick detection element.