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 Transformermodels often struggle to obtain long-range contextual understanding while maintaining computationalefficiency. This research proposes a new method named PVT Fusion Mamba architecture, whichintegrates PVT-v2-B2 and EfficientNet-B0 in a dual-encoder backbone, followed by a hierarchical fusionneck and a Mamba-based classification head. This architecture enables effective multi-scale feature integrationand efficient long-range dependency modeling with linear complexity, while dynamically emphasizingorganism-relevant features and suppressing background noise. We conducted experiments usingthe ROUD Dataset across ten marine organism classes. An extensive ablation study confirmed the synergisticeffect of the dual-encoder fusion, demonstrating that the combined PVT Fusion Mamba architecturesignificantly outperforms its single-encoder counterparts (EfficientNet-B0 and PVT-v2-B2) in terms of convergencespeed and final accuracy. Furthermore, in comparative studies against models like ResNet50 andYOLOv8, our proposed architecture achieved superior performance. The PVT Fusion Mamba architectureattained state-of-the-art accuracy of 98.6% with an optimized validation loss of 0.062 (at configurationC1 = 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 demonstratethat PVT Fusion Mamba successfully overcomes the limitations of previous methods, achieving superioraccuracy and robustness with reduced computational cost compared to established deep learning models.