Automated detection of submarine pipelines in the Yellow River Estuary: a deep learning approach for side-scan sonar data in dynamic deltaic systems

The integrity of submarine pipelines and cables is crucial for safeguarding marine oil, gas, and information transmission, as well as ecological security. Employing automated identification of side-scan sonar (SSS) images can enhance marine geophysical survey efficiency, enabling high-frequency assessment of seabed anthropogenic footprints. However, there is a notable gap in research regarding the comparative performance of different models and the impact of data expansion. This study presents an in-depth comparison of various convolutional neural network (CNN) models-specifically, AlexNet, GoogleNet, and VGG-16-focusing on their prediction accuracy and computational efficiency in analyzing SSS datasets. Our findings reveal that GoogleNet outperforms the others, offering superior prediction accuracy with balanced computational demands. While AlexNet is less accurate, it is beneficial for scenarios with limited computational resources. Conversely, VGG-16 shows comparatively weaker performance, making it less suitable for SSS image analysis. Notably, data expansion significantly influences model accuracy, although its impact varies across different models. This research contributes critical insights into model selection for marine geological applications, demonstrating the potential of intelligent interpretation systems in modern marine geology.