A Hybrid Multi-Backbone Learning Framework for Marine Pollution Detection Using Deep Feature Fusion
TL;DR
Evidence is provided that transformer-based deep learning approaches can be effectively applied to marine pollution detection and demonstrate promising performance in environmental monitoring tasks.
Abstract
Marine ecosystems are increasingly threatened by increasing industrial activities and anthropogenic environmental impacts. Especially oil spills and chemical wastes cause serious pollution in the seas. This situation negatively affects both marine life and economic activities. In this context, automatic and early detection of marine pollution is of great importance for the effectiveness of environmental response processes. In this study, a hybrid approach combining Transformer-based feature extraction and deep neural networks is proposed for image-based marine pollution classification. Advanced visual feature extraction models such as BEiT, ViT, DeiT, SwinV2, ConvNeXt and DTFC-Net are used and the features extracted from these models are trained with a deep neural network classifier. Model performance is evaluated on two different public datasets using both 5-fold cross-validation and hold-out method. According to the results, all models achieved high accuracy values; however, the proposed DTFC-Net model stood out by achieving the highest accuracy (98.60%, 98.33%) on both datasets. These findings provide evidence that transformer-based deep learning approaches can be effectively applied to marine pollution detection and demonstrate promising performance in environmental monitoring tasks.