On Vision Transformers for Classification Tasks in Side-Scan Sonar Imagery
This work addresses the challenge of automated classification in underwater environments for applications like marine archaeology or defense, but it is incremental as it applies an existing method (ViTs) to a new domain with a comparative analysis.
The paper tackled the problem of classifying man-made objects in side-scan sonar imagery by comparing Vision Transformers (ViTs) with CNN architectures like ResNet and ConvNext, finding that ViT-based models achieved superior performance in metrics such as f1-score, precision, recall, and accuracy, though with higher computational costs.
Side-scan sonar (SSS) imagery presents unique challenges in the classification of man-made objects on the seafloor due to the complex and varied underwater environments. Historically, experts have manually interpreted SSS images, relying on conventional machine learning techniques with hand-crafted features. While Convolutional Neural Networks (CNNs) significantly advanced automated classification in this domain, they often fall short when dealing with diverse seafloor textures, such as rocky or ripple sand bottoms, where false positive rates may increase. Recently, Vision Transformers (ViTs) have shown potential in addressing these limitations by utilizing a self-attention mechanism to capture global information in image patches, offering more flexibility in processing spatial hierarchies. This paper rigorously compares the performance of ViT models alongside commonly used CNN architectures, such as ResNet and ConvNext, for binary classification tasks in SSS imagery. The dataset encompasses diverse geographical seafloor types and is balanced between the presence and absence of man-made objects. ViT-based models exhibit superior classification performance across f1-score, precision, recall, and accuracy metrics, although at the cost of greater computational resources. CNNs, with their inductive biases, demonstrate better computational efficiency, making them suitable for deployment in resource-constrained environments like underwater vehicles. Future research directions include exploring self-supervised learning for ViTs and multi-modal fusion to further enhance performance in challenging underwater environments.