Enhancing River Waste Detection with Deep Learning and Preprocessing: A Case Study in the Urban Canals of the Chao Phraya River

Abstract

Plastic waste in river systems represents a major pathway of marine pollution, with rivers estimated to contribute up to 80% of the plastic entering the ocean. This study introduces a deep learning framework with preprocessing for automated detection and tracking of floating plastic waste (macroplastics) in the urban canals of the Chao Phraya River, Thailand. Unlike previous approaches that rely on site-specific retraining or model modification, our method employs a YOLO-based detection model integrated with DeepSORT (Deep Simple Online and Realtime Tracking). The model, initially trained on laboratory flume images, was adapted to real river conditions through a three-step preprocessing pipeline comprising skew correction, background removal, and object region extraction. Experiments on 2000 canal images demonstrated that preprocessing improved the mean Average Precision (mAP) from 0.74 to 0.85, with notable gains for categories such as foam and paper. Testing with a more advanced YOLO architecture further enhanced accuracy, indicating that preprocessing and model upgrades are complementary. These findings suggest that reliable detection and quantification of floating waste can be achieved without retraining. The proposed framework provides a scalable and cost-effective solution for monitoring in data-limited regions, contributing to efforts to mitigate riverine and marine plastic pollution. Future work will address the remaining limitations, as detection performance is still influenced by strong reflections, motion blur, and occlusion, occasionally resulting in missed detections.