# What's Mine is Yours: Pretrained CNNs for Limited Training Sonar ATR

arXiv: https://arxiv.org/abs/1706.09858 · HTML: https://mlsanity.com/paper/1706.09858/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2017.

## Built on by (most cited first, 7 of 7)

- [SeafloorAI: A Large-scale Vision-Language Dataset for Seafloor Geological Survey](https://mlsanity.com/paper/2411.00172/) (2024)
- [Target Detection and Segmentation in Circular-Scan Synthetic-Aperture-Sonar Images using Semi-Supervised Convolutional Encoder-Decoders](https://mlsanity.com/paper/2101.03603/) (2021)
- [Improving Automated Sonar Video Analysis to Notify About Jellyfish Blooms](https://mlsanity.com/paper/2103.04068/) (2021)
- [Advances in Self-Supervised Learning for Synthetic Aperture Sonar Data Processing, Classification, and Pattern Recognition](https://mlsanity.com/paper/2308.11633/) (2023)
- [Mine-JEPA: In-Domain Self-Supervised Learning for Mine-Like Object Classification in Side-Scan Sonar](https://mlsanity.com/paper/2604.00383/) (2026)
- [External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery](https://mlsanity.com/paper/2107.10504/) (2021)
- [Underwater SONAR Image Classification and Analysis using LIME-based Explainable Artificial Intelligence](https://mlsanity.com/paper/2408.12837/) (2024)

## Abstract (arXiv)

Finding mines in Sonar imagery is a significant problem with a great deal of relevance for seafaring military and commercial endeavors. Unfortunately, the lack of enormous Sonar image data sets has prevented automatic target recognition (ATR) algorithms from some of the same advances seen in other computer vision fields. Namely, the boom in convolutional neural nets (CNNs) which have been able to achieve incredible results - even surpassing human actors - has not been an easily feasible route for many practitioners of Sonar ATR. We demonstrate the power of one avenue to incorporating CNNs into Sonar ATR: transfer learning. We first show how well a straightforward, flexible CNN feature-extraction strategy can be used to obtain impressive if not state-of-the-art results. Secondly, we propose a way to utilize the powerful transfer learning approach towards multiple instance target detection and identification within a provided synthetic aperture Sonar data set.