Automatic recognition of underwater munitions from multi-view sonar surveys using semi supervised machine learning: a simulation study

This paper presents a machine learning technique for using large unlabelled survey datasets to aid automatic classification. We have demonstrated the benefit of this technique on a simulated synthetic aperture sonar (SAS) dataset. We designed a machine learning model to encode a representation of SAS images from which new SAS views can be generated. This novel task requires the model to learn the physics and content of SAS images without the requirement for human labels. This is called self-supervised learning. The pre-trained model can then be fine-tuned to perform classification on a small amount of labelled examples. This is called semi-supervised learning. By using a simulated dataset we can step-by-step increase the realism to identify the sources of difficulty for applying this method to real SAS data, and have a performance bench mark from this more idealised dataset. We have quantified the improved accuracy for the re-view model (ours), against a traditional self-supervised approach (autoencoder), and no pre-training. We have also demonstrated generating novel views to qualitatively inspect the model's learned representation. These results demonstrate our re-view self-supervised task aids the downstream classification task and model interpretability on simulated data, with immediate potential for application to real-world UXO monitoring

Automatic recognition of underwater munitions from multi-view sonar surveys using semi supervised machine learning: a simulation study

This paper presents a machine learning technique for using large unlabelled survey datasets to aid automatic classification. We have demonstrated the benefit of this technique on a simulated synthetic aperture sonar (SAS) dataset. We designed a machine learning model to encode a representation of SAS images from which new SAS views can be generated. This novel task requires the model to learn the physics and content of SAS images without the requirement for human labels. This is called self-supervised learning. The pre-trained model can then be fine-tuned to perform classification on a small amount of labelled examples. This is called semi-supervised learning. By using a simulated dataset we can step-by-step increase the realism to identify the sources of difficulty for applying this method to real SAS data, and have a performance bench mark from this more idealised dataset. We have quantified the improved accuracy for the re-view model (ours), against a traditional self-supervised approach (autoencoder), and no pre-training. We have also demonstrated generating novel views to qualitatively inspect the model's learned representation. These results demonstrate our re-view self-supervised task aids the downstream classification task and model interpretability on simulated data, with immediate potential for application to real-world UXO monitoring

Survey on deep learning based computer vision for sonar imagery

Research on the automatic analysis of sonar images has focused on classical, i.e. non deep learning based, approaches for a long time. Over the past 15 years, however, the application of deep learning in this research field has constantly grown. This paper gives a broad overview of past and current research involving deep learning for feature extraction, classification, detection and segmentation of sidescan and synthetic aperture sonar imagery. Most research in this field has been directed towards the investigation of convolutional neural networks (CNN) for feature extraction and classification tasks, with the result that even small CNNs with up to four layers outperform conventional methods. The purpose of this work is twofold. On one hand, due to the quick development of deep learning it serves as an introduction for researchers, either just starting their work in this specific field or working on classical methods for the past years, and helps them to learn about the recent achievements. On the other hand, our main goal is to guide further research in this field by identifying main research gaps to bridge. We propose to leverage the research in this field by combining available data into an open source dataset as well as carrying out comparative studies on developed deep learning methods.Article number 10515711

Self-Supervised Learning for Improved Synthetic Aperture Sonar Target Recognition

This study explores the application of self-supervised learning (SSL) for improved target recognition in synthetic aperture sonar (SAS) imagery. The unique challenges of underwater environments make traditional computer vision techniques, which rely heavily on optical camera imagery, less effective. SAS, with its ability to generate high-resolution imagery, emerges as a preferred choice for underwater imaging. However, the voluminous high-resolution SAS data presents a significant challenge for labeling; a crucial step for training deep neural networks (DNNs). SSL, which enables models to learn features in data without the need for labels, is proposed as a potential solution to the data labeling challenge in SAS. The study evaluates the performance of two prominent SSL algorithms, MoCov2 and BYOL, against the well-regarded supervised learning model, ResNet18, for binary image classification tasks. The findings suggest that while both SSL models can outperform a fully supervised model with access to a small number of labels in a few-shot scenario, they do not exceed it when all the labels are used. The results underscore the potential of SSL as a viable alternative to traditional supervised learning, capable of maintaining task performance while reducing the time and costs associated with data labeling. The study also contributes to the growing body of evidence supporting the use of SSL in remote sensing and could stimulate further research in this area