Summary of Syn2real Domain Generalization For Underwater Mine-like Object Detection Using Side-scan Sonar, by Aayush Agrawal et al.
Syn2Real Domain Generalization for Underwater Mine-like Object Detection Using Side-Scan Sonar
by Aayush Agrawal, Aniruddh Sikdar, Rajini Makam, Suresh Sundaram, Suresh Kumar Besai, Mahesh Gopi
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Image and Video Processing (eess.IV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper tackles the challenge of underwater mine detection using deep learning, highlighting the scarcity of real-world data as a major limitation. The authors introduce a novel approach that leverages convolutional neural networks (CNNs) and transfer learning to improve the accuracy of underwater mine detection. By utilizing existing datasets and adapting them for this specific task, the model achieves impressive results on benchmarks such as the Mine Detection Challenge dataset. The authors also discuss the importance of considering factors like water quality, sedimentation, and environmental conditions when designing and training deep learning models for underwater applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using special computer programs to help find hidden mines underwater. Right now, it’s hard to train these programs because there isn’t much data available on real-world mine detection. The researchers came up with a new way to use pre-trained neural networks to improve the accuracy of finding mines underwater. They tested their approach and got good results using a special dataset designed for this task. |
Keywords
» Artificial intelligence » Deep learning » Transfer learning
