Fusion-Based Machine Learning Approach for Classification of Seabed Defects

Abstract

Accurate classification of the seabed defects is crucial in safeguarding the safety, reliability, and maintenance of underwater infrastructure, including pipelines, cables, and offshore platforms. Classification of underwater images remains a challenging task due to issues such as low visibility, scattering, color distortion, and multi-scale variations in underwater environments. To address these challenges, this study proposes IEResViT, a novel fusion-based hybrid architecture that integrates a Depthwise Inception Convolutional Neural Network (CNN) with a modified Vision Transformer (ViT) framework. The proposed model replaces the traditional multilayer perceptron (MLP) block in the transformer encoder with a ResNet-based residual connection (ResMLP) to enhance hierarchical feature extraction and reduce computational complexity. The architecture processes images through parallel CNN and transformer branches to capture both local and global feature representations, followed by feature fusion and classification. The proposed model was evaluated on the Aquatic Defect dataset, AQUA20 dataset and Marine_PULSE dataset, achieving high classification performance with an accuracy of 98.88% 98.65% and 95.65% respectively, outperforming several state-of-the-art deep learning models while using fewer parameters and reduced computational cost. The results demonstrate that the IEResViT model provides an efficient and lightweight solution for reliable underwater defect image classification.