Summary of Terrain Characterisation For Online Adaptability Of Automated Sonar Processing: Lessons Learnt From Operationally Applying Atr to Sidescan Sonar in Mcm Applications, by Thomas Guerneve et al.
Terrain characterisation for online adaptability of automated sonar processing: Lessons learnt from operationally applying ATR to sidescan sonar in MCM applications
by Thomas Guerneve, Stephanos Loizou, Andrea Munafo, Pierre-Yves Mignotte
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Robotics (cs.RO); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of deploying Automated Recognition (ATR) algorithms on side-scan sonar imagery in non-benign environments, where complex seafloors and acoustic artefacts can create false detections or prevent true object detection. To improve explainability during Autonomous Underwater Vehicles (AUVs) missions, the authors propose two online seafloor characterisation techniques that rely on unsupervised machine learning approaches to extract terrain features related to human understanding of terrain complexity. The first technique provides a quantitative metric for application-driven terrain characterisation based on ATR algorithm performance, while the second method incorporates subject matter expertise and enables contextualisation and explainability. These techniques are suitable for real-time onboard processing and can be applied to scenarios such as Mine Countermeasures (MCM) missions. By leveraging these methods, AUVs can improve their ability to characterise seafloor terrain complexity, making them more desirable and trustworthy in comparison to traditional approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how machines can better recognize what’s on the sea floor when there are lots of distractions like rocks or noise. Right now, these machines have trouble finding things they’re supposed to find because of all these distractions. To fix this problem, the authors came up with two new ways for these machines to look at the sea floor and figure out what’s going on. These methods use special computer programs that can learn from what they see, without needing humans to tell them what to do. This means that these machines can make decisions on their own while still being able to explain why they’re making those decisions. The authors also show how these methods can be used in real-life situations like finding mines at sea. |
Keywords
» Artificial intelligence » Machine learning » Object detection » Unsupervised
