A 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. Underwater infrastructure, such as pipelines, communication cables, offshore wind farms, and marine habitats, is a crucial interface on the seabed. Natural processes and human activities may cause seabed defects, e.g., cracks and erosion scars, or sediment liquefaction areas or uncovered buried utilities, over time. Early and precise diagnosis of these defects is critical in preventive maintenance, environmental safety, and navigation. Traditional seabed relies on manual interpretation, and its time consuming and labour-intensive. Recent advancements in machine learning help in automated detection. The graphical abstract presents a deep learning model to classify the aquatic defect images. This article presents the IEResViT model, which uses two parallel branches: a depthwise inception CNN branch that extracts local spatial features, and a ResMLP ViT branch that captures global dependencies through multi-head self-attention mechanisms. The extracted local and global features are then fused together, enabling enhanced representation of complex underwater defect image characteristics. The fused features are subsequently passed to fully connected dense layers, followed by a softmax classifier to predict the defect category. The Aquatic defect image dataset, AQUA20 dataset, and Marine_PULSE dataset are used to evaluate the performance of the model, and achieved an accuracy rate of 98.88%, 98.65%, and 95.65% respectively.