Plastic pollution in rivers is a growing environmental issue with widespread impacts. Monitoring the movement of plastic waste across different river systems is challenging due to environmental variability and the limited availability of labeled data. This thesis investigates camera-based methods for detecting floating macroplastics in rivers and explores ways to adapt detection systems to new locations with minimal data. Collecting data from the Limmat River in Zurich and the uMhlangane River in Durban, South Africa, the study assesses the impact of domain shifts on detection performance and proposes a semi-automated annotation pipeline to improve labeling. Furthermore, it tests techniques like few-shot learning and pseudolabeling to address the performance dip. The results show that model performance decreases significantly when applied to new locations but that even with minimal data, camera-based monitoring can provide useful insights for understanding waste movement and informing plastic waste management strategies.