Swin Transformer-Based Framework for Floating Marine Debris Detection and Classification

[TechRxiv] Yahia Hamdi, Mohamed Fnadi, Wenqian Du and others proposed a Swin Transformer-based deep learning framework for detecting and classifying floating debris in water surface debris accumulation areas such as rivers, coastlines and ports. The study performed Bayesian optimization and fine-tuning on Swin-T and compared it with ResNet-50, InceptionV3, VGG-19 and ViT; the dataset used was expanded from public data and newly collected RGB images under multiple hydrological and lighting conditions. The paper claims that the optimized Swin-T achieves a classification accuracy of 98.5% and a detection mAP of 52.3%, outperforming baseline Transformers and conventional CNNs. The authors state that systematic hyperparameter optimization can significantly improve the performance of Transformer models for marine debris monitoring.