Intelligent airborne monitoring of irregularly shaped man-made marine objects using statistical Machine Learning techniques

Abstract

The marine economy has historically been highly diversified and prolific due to the fact that the Earth's oceans comprise two-thirds of its total surface area. As technology advances, leading enterprises and ecological organisations are building and mobilising new devices supported by cutting-edge marine mechatronics solutions to explore and harness this challenging environment. Automated tracking of these types of industries and the marine life around them can help us figure out what's causing the current changes in species numbers, predict what could happen in the future, and create the right policies to help reduce the environmental impact and make the planet more sustainable. The objective of this study is to create a new platform for the automated detection of irregularly shaped man-made marine objects (ISMMMOs) in large datasets derived from marine aerial survey imagery. In this context, a novel nonparametric methodology, which harbours several hybrid statistical Machine Learning (ML) methods, was developed to automatically segment ISMMMOs on the sea surface in large surveys. This methodology was validated on a wide range of marine domains, providing robust empirical proof of concept. This approach enables the detection of ISMMMOs automatically, without any prior training, with accuracy (ACC), Matthews correlation coefficient (MCC), negative predictive value (NPV), positive predictive value (PPV), specificity (Sp) and sensitivity(Se) over 0.95. The outlined methodology can be utilised for a variety of purposes, but it's especially useful for researchers and policymakers who want to keep an eye on how the maritime industry is deploying and make sure the right policies are in place to meet regulatory and legal requirements to promote maritime tech innovation and shape what the future looks like for the marine ecosystem. For the first time in the literature, a method, the so-called ISMMMOD, has been developed to automate the detection of all types of ISMMMOs by statistical ML techniques that require no prior training, which will pioneer the monitoring of human footprint in the marine ecosystem.