To solve this problem, the deep learning procedure was carried out for real images of marine fuel slicks, which were detected at different times in the coastal zone of Peter the Great Bay. The second stage of adaptation of the computer vision method for monitoring oil slicks on the sea surface is the development of an artificial intelligence (AI) element for autonomous segmentation and classification of slicks. In this work, we consider only thin slicks of oil products, because the method of the oil appearance code (BAOAC) may be applied and was adopted in the Bonn Agreement for such slicks [16; 17]. Estimates of slick thickness and pollution volumes can be made by dividing real thin slicks into color-coded segments. This approach is only valid for thin slicks of oil products and cannot be used to monitor crude oil slicks, unlike an AI element for slick detection. The second task was to adapt the LIF method for a drone. The method should provide the possibility of measuring the LIF spectrum when the drone hovers overhead the slick and of measuring the LIF spectrum in the subsurface layer to register dissolved oil products in seawater and measure their concentration. A small-sized laser LIF spectrometer was developed, which can be used on a small-sized drone, to solve this task. The AI element is necessary for an autonomous determination of the type of oil products contained in a slick or a dissolved state, as well as a determination of the concentration of solutions in the latter case. Collection of appropriate datasets, including LIF spectra from various oil products, and appropriate calibrations are required to determine the concentration of solutions of oil product in seawater. The third task of this work was to develop the carrier itself--a drone that adapts to monitoring tasks and should provide monitoring in various modes: passive optical monitoring of the sea surface using computer vision, active laser sensing of the sea surface,or subsurface sensing using LIF spectroscopy. The specificity of such a drone is autonomy of the monitoring mission, including in areas remote from the coastal zone, and a vessel. Therefore, the software (drone artificial intelligence) is designed for autonomous navigation and motion control, the ability of autonomous landing on water, and a moving object. Thus, all three tasks were combined in the development of a hardware and software framework for the drone (HSFW), which includes all hardware for passive and active monitoring, software that provides autonomous monitoring, autonomous navigation, and flight control of the drone, and the drone itself--the carrier of this hardware and software framework. Due to the complexity of the hardware and software parts, the framework was divided into three separate but interacting sub-frameworks, which include hardware and software, combined according to their functionality: the hardware and software sub-framework of passive optical monitoring (drone computer vision)--HSFW1, the hardware and software sub-framework of active LIF spectroscopy--HSFW2, and the hardware and software sub-framework of navigation and drone motion control--HSFW3. Here and below, the term framework includes hardware, since the hardware is combined into complexes to reach one functional purpose, but which can change and expand depending on the specific conditions and monitoring tasks. Thus, for example, in the simplest version of the autopilot, when there is a stable GNSS signal, there is no need for a camera to determine the drone position. However, in the case of a poor GNSS signal, which often occurs in shelf waters, far from ports (and especially in high latitudes), a local positioning system is necessary to be installed on the drone for an autonomous monitoring mission (SLAM system [18]). This system is based on linking to any image, for example, to a vessel from which a drone takes off. In this case, an additional camera and corresponding software are necessary to be added to the HSFW3, which is functionally designed for autonomous navigation.