In December 2023, 25 tons of pellets were lost by the Toconao ship in the Northwest Atlantic Ocean in front of the Portuguese coast. In this work, a coastal stretch of 633 km in Asturias and Galicia (Spain) and Northern Portugal was investigated to assess pellets' concentration on 31 beaches. Field surveys were carried out in March 2024 and focused on sampling plastic pellets deposited along the shoreline. All the 7263 sampled pellets were characterized by size, degradation level, and color, while one subset was characterized by weight (40 % of the total) and another subset by polymer type (15 % of the total) using FT-IR spectroscopy. The results reveal that 94 % of the surveyed sites containing pellets, whereas the concentration values vary significantly among beaches, ranging from 0 to 40.3 pellets/kg_dw. By combining the accounted variables, it emerges that 48.0 % of the collected pellets can be linked to the Toconao spill.

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.

This paper presents an approach to improving visual object tracking performance by dynamically fusing the results of two trackers, where the scheduling of trackers is determined by a support vector machine (SVM). By classifying the outputs of other trackers, our method learns their behaviors and exploits their complementarity to enhance tracking accuracy and robustness. Our approach consistently surpasses the performance of individual trackers within the ensemble. Despite being trained on only 4 sequences and tested on 144 sequences from the VOTS2023 benchmark, our approach achieves a Q metric of 0.65. Additionally, our fusion strategy demonstrates versatility across different datasets, achieving 73.7 MOTA on MOT17 public detections and 82.8 MOTA on MOT17 private detections. On the MOT20 dataset, it achieves 68.6 MOTA on public detections and 79.7 MOTA on private detections, setting new benchmarks in multi-object tracking. These results highlight the potential of using an ensemble of trackers with a learner-based scheduler to significantly improve tracking performance.