Abstract
This paper presents a system for the intelligent search of shipwrecks using Autonomous Underwater Vehicles (AUVs). It introduces a machine learning approach to the automatic identification of potential archaeological sites from AUV-obtained side-scan sonar (SSS) data. The site identification pipeline consists of a series of stages that set up for, run, and process the output of a convolutional neural network (CNN). To alleviate the issue of training data scarcity, i.e. the lack of SSS data that includes shipwrecks, and improve the performance at testing time, a data augmentation stage is included in the pipeline. In addition, edge detection and other traditional image processing feature extraction methods are used in parallel with CNN to improve algorithmic performance. Experiments from two multi-deployment shipwreck search expeditions involving actual AUV deployments along the coast of Malta for data collection and processing demonstrate the pipeline’s usefulness. Results from these two field expeditions yielded a precision/recall of 29.34%/97.22% and 32.95%/80.39%, respectively. Despite the poor precision, the pipeline filters out 99.79% of the area in data set A and 99.31% of the area in data set B.
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Acknowledgements
We would like to thank Jeffrey Rutledge, Samantha Ting, Russell Bingham, Andrew Pham, Kolton Yager, Bonita Galvan, and Mitchell Keller for helping us deploy the robot. This work was performed in part at Claremont Colleges Robert J. Bernard Biological Field Station. This material is based upon work supported by National Science Foundation under Grant No. 1460153.
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Nayak, N., Nara, M., Gambin, T., Wood, Z., Clark, C.M. (2021). Machine Learning Techniques for AUV Side-Scan Sonar Data Feature Extraction as Applied to Intelligent Search for Underwater Archaeological Sites. In: Ishigami, G., Yoshida, K. (eds) Field and Service Robotics. Springer Proceedings in Advanced Robotics, vol 16. Springer, Singapore. https://doi.org/10.1007/978-981-15-9460-1_16
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