PVT-EfficientNet Dual Encoder with Mamba Head for Efficient UnderwaterImage Classification - Frisky, Aufaclav Zatu Kusuma; Hartanto, Ari Dwi; Fatmah, Hanum Khairana; Siva, Fadillah; Maaroja, Waffiq; Indarto, Novelio Putra; Tuasikal, Mohammad Akbar Ghifari; Wang, Jia Ching; Siva, Fadillah (Informatica, 2026)
Identification and classification of marine organisms remain challenging due to pose variation, partial occlusions, and underwater imaging conditions. Existing Convolutional Neural Network (CNN) and Transformer models often struggle to obtain long-range contextual understanding while maintaining computational efficiency. This research proposes a new method named PVT Fusion Mamba architecture, which integrates PVT-v2-B2 and EfficientNet-B0 in a dual-encoder backbone, followed by a hierarchical fusion neck and a Mamba-based classification head. This architecture enables effective multi-scale feature integration and efficient long-range dependency modeling with linear complexity, while dynamically emphasizing organism-relevant features and suppressing background noise. We conducted experiments using the ROUD Dataset across ten marine organism classes. An extensive ablation study confirmed the synergistic effect of the dual-encoder fusion, demonstrating that the combined PVT Fusion Mamba architecture significantly outperforms its single-encoder counterparts (EfficientNet-B0 and PVT-v2-B2) in terms of convergence speed and final accuracy. Furthermore, in comparative studies against models like ResNet50 and YOLOv8, our proposed architecture achieved superior performance. The PVT Fusion Mamba architecture attained state-of-the-art accuracy of 98.6% with an optimized validation loss of 0.062 (at configuration C1 = 96,C2 = 288,C3 = 192). Analysis of the confusion matrix reveals excellent classification performance, with most errors occurring only between morphologically similar species. The results demonstrate that PVT Fusion Mamba successfully overcomes the limitations of previous methods, achieving superior accuracy and robustness with reduced computational cost compared to established deep learning models.