Assessment of Spectral based Solutions for the Detection of Floating Marine Debris
This work addresses the need for standardized evaluation in marine debris detection, which is incremental as it builds on existing datasets and methods.
The study tackled the problem of detecting floating marine debris by evaluating spectral-based remote sensing solutions on the MARIDA dataset, revealing that precise references are crucial for fair performance assessment.
Typically, the detection of marine debris relies on in-situ campaigns that are characterized by huge human effort and limited spatial coverage. Following the need of a rapid solution for the detection of floating plastic, methods based on remote sensing data have been proposed recently. Their main limitation is represented by the lack of a general reference for evaluating performance. Recently, the Marine Debris Archive (MARIDA) has been released as a standard dataset to develop and evaluate Machine Learning (ML) algorithms for detection of Marine Plastic Debris. The MARIDA dataset has been created for simplifying the comparison between detection solutions with the aim of stimulating the research in the field of marine environment preservation. In this work, an assessment of spectral based solutions is proposed by evaluating performance on MARIDA dataset. The outcome highlights the need of precise reference for fair evaluation.