Advancing deep learning-based detection of floating litter using a novel open dataset - Jia, Tianlong; Vallendar, Andre Jehan; de Vries, Rinze; Kapelan, Zoran; Taormina, Riccardo (Frontiers in Water, 2023)
Supervised Deep Learning (DL) methods have shown promise in monitoring the floating litter in rivers and urban canals but further advancements are hard to obtain due to the limited availability of relevant labelled data. To address this challenge, researchers often utilize techniques such as transfer learning (TL) and data augmentation (DA). However, there is no study currently reporting a rigorous evaluation of the effectiveness of these approaches for floating litter detection and their effects on the models' generalization capability. To overcome the problem of limited data availability, this work introduces the "TU Delft -Green Village" dataset, a novel labelled dataset of 9473 camera and phone Jia et al.