## 6 Testing and Results
### Testing of HSFW1  
The HSFW1 test results reported in this article were obtained by monitoring three different fuel slicks that occurred while refueling vessels. In all cases, measures were taken to eliminate slicks, but part of the fuel passed through boom barriers and ended up in open-water areas. Drone monitoring was carried out on the next day after the slicks. We were able to track the dynamics of the slicks and estimate the amount of fuel remaining after disposal operations.  
In all cases, monitoring was carried out in the daytime in the absence of rough seas. There was a stable reception of GNSS signals for autonomous navigation and flight control using GNSS. The flight route and the initial altitude (from 50 to 100 m, depending on weather conditions) of the copter were set by the operator before departure.  
In the process of flying around the water area, the HSFW1 element detected all spots visited by the copter along the route. The flight route (see Figure 7) was organized in such a way that the entire water area planned for monitoring was analyzed by the HSFW1 detection element.  
Then, the classification element determined the thickness of the slicks on the basis of the color codes and calculated the corresponding areas of the slick segments. Next, the total volume of marine fuel in slicks was calculated. Determination of the effectiveness of those measures will allow eliminating pollution with light types of marine fuel while the drone is flying. Approximately 15% to 20% of the total volume of pollution fell into open-water areas after the initial response to the slick.  
The main problems faced by the HSFW1 shell during autonomous detection were associated either with an insufficient number of images in the dataset used to train the neural net or with the fact that the characteristics of the camera and computer did not allow reducing the image processing time. Thus, the detection element of HSFW1 recognized spots well when they were observed directly under the drone or at a small angle to the surface normal. Figure 10a shows the spot at an angle of observation to the normal to the surface of about 45\({}^{\circ}\). HSFW1 determined the probability of detecting a slick as only 0.5, i.e., below the threshold we set. HSFW1 detected a slick with a probability of 0.95 (Figure 10b) when the image of the same slick region was analyzed, at a very small angle to the vertical to the surface.  
Thus, oil slicks, which were away from the flight path and observed at angles greater than 45%, were usually skipped by the HSFW1 element. As a consequence, HSFW3 did not adjust the flight route. A significant replenishment of datasets with images captured from different angles to the normal to the surface is necessary for the correct use of HSFW1. This also applies to the classification element HSFW1, where, in addition to the abovementioned difficulties, factors that affect the surface color add significant complexity. A large expansion of datasets is necessary due to the images of slicks in different lighting conditions and different weather conditions for a more correct operation of the classification element of HSFW1.  
Figure 10: Detection: (**a**) spot detection at an observation angle of about 45\({}^{\circ}\); (**b**) probability of detection when a slick is located directly under the drone camera (nadir).