Application of Direct and Indirect Methodologies for Beach Litter Detection in Coastal Environments

In this study, different approaches for detecting of beach litter (BL) items in coastal environments are applied: the direct in situ survey, an indirect image analysis based on the manual visual screening approach, and two different automatic segmentation and classification tools. One is a Mask-RCNN based-algorithm, already used in a previous work, but specifically improved in this study for multi-class analysis. Test cases were carried out at the Torre Guaceto Marine Protected Area (Apulia Region, southern Italy), using a novel dataset from images acquired in different coastal environments by tailored photogrammetric Unmanned Aerial Vehicle (UAV) surveys. The analysis of the overall methodologies used in this study highlights the potential exhibited by the two machine learning (ML) techniques (Mask-RCCN-based and SVM algorithms), but they still show some limitations concerning direct methodologies. The results of the analysis show that the Mask-RCNN-based algorithm requires further improvements and a consistent increase in the number of training elements, while the SVM algorithm shows limitations related to pixel-based classification. Furthermore, the outcomes of this research highlight the high suitability of ML tools for assessing BL pollution and contributing to coastal conservation efforts.