Development of the Artificial Intelligence and Optical Sensing Methods for Oil Pollution Monitoring of the Sea by Drones

Abstract

The oil pollution of seas is increasing, especially in local areas, such as ports, roadsteads of the vessels, and bunkering zones. Today, methods of monitoring seawater are costly and applicable only in the case of big ecology disasters. The development of an operative and reasonable project for monitoring the sea surface for oil slick detection is described in this article using drones equipped with optical sensing and artificial intelligence. The monitoring system is implemented in the form of separate hard and soft frameworks (HSFWs) that combine monitoring methods, hardware, and software. Three frameworks are combined to fulfill the entire monitoring mission. HSFW1 performs the function of autonomous monitoring of thin oil slicks on the sea surface, using computer vision with AI elements for detection, segmentation, and classification of thin slicks. HSFW2 is based on the use of laser-induced fluorescence (LIF) to identify types of oil products that form a slick or that are in a dissolved state, as well as measure their concentration in solution. HSFW3 is designed for autonomous navigation and drone movement control. This article describes AI elements and hardware complexes of the three separate frameworks designed to solve the problems with monitoring slicks of oil products on the sea surface and oil products dissolved in seawater. The results of testing the HSFWs for the detection of pollution caused by marine fuel slicks are described.

Toward Remote Detection of Chemical Warfare Simulants Using a Miniature Potentiostat

“…Yet, the field is emerging, and many sensors that have already been fitted to the hardware of a UAV are still in the proof-of-concept phase, and further refinement and validation are needed. Most of the current sensors that have been implemented into drone technology are either optical sensors [44] or miniature mass spectrometers [45] or have infrared capabilities [46]. By successfully demonstrating that reliable electrochemical results can be produced using a miniaturized potentiostat, we believe that this contribution can advance the field by incorporating a different sensing paradigm onto UAVs and the field writ large.…”

Uncrewed Aerial Systems in Water Resource Management and Monitoring: A Review of Sensors, Applications, Software, and Issues

“…Tis study hierarchically used satellite, helicopter, and drone data to monitor water quality in pollution scenarios. Consumergrade UAVs can be used to detect these pollutants [221], particularly when used in conjunction with machine learning [222] and deep learning techniques [144,[223][224][225][226]. However, these automated approaches still need to be improved [222].…”

UAV-Based Subsurface Data Collection Using a Low-Tech Ground-Truthing Payload System Enhances Shallow-Water Monitoring

“…Once the underwater camera stops detecting the habitat of interest in one direction or records a depth above or below a preset value, the flight path can automatically be adjusted to encircle the area of interest while saving valuable survey time, increasing the amount of class-specific point observations and reducing the amount of noise in the collected data set. This approach of dynamic and intelligent autonomous flight navigation and data collection has been tested in many applications [64][65][66][67] and could likewise increase the efficiency of area-based monitoring missions by, for example, limiting the data collection to areas where the UAV mounted sensor still receives useful information and at the same time indicating where subsurface data collection is required.…”