论文标题
声纳图像分类的自学学习
Self-supervised Learning for Sonar Image Classification
论文作者
论文摘要
事实证明，自我监督的学习被证明是学习图像表示的强大方法，而无需大型标记的数据集。对于水下机器人技术，设计计算机视觉算法以提高感知功能（例如声纳图像分类）非常感兴趣。由于声纳成像的机密性质和解释声纳图像的困难，创建公共标记的声纳数据集以培训监督的学习算法是一项挑战。在这项工作中，我们研究了三种自制学习方法的潜力（ROTNET，DENOSING AUTOCODODERS和JIGSAW），不需要人类标签，学习高质量的声纳图像表示。我们对现实生活中的声纳图像数据集提出了预培训和转移学习结果。我们的结果表明，自我监管的预训练的产量分类性能可与所有三种方法中的几次转移学习设置中的监督预训练相当。 https://github.com/agrija9/ssl-sonar-images可用代码和自我监管的预培训模型
Self-supervised learning has proved to be a powerful approach to learn image representations without the need of large labeled datasets. For underwater robotics, it is of great interest to design computer vision algorithms to improve perception capabilities such as sonar image classification. Due to the confidential nature of sonar imaging and the difficulty to interpret sonar images, it is challenging to create public large labeled sonar datasets to train supervised learning algorithms. In this work, we investigate the potential of three self-supervised learning methods (RotNet, Denoising Autoencoders, and Jigsaw) to learn high-quality sonar image representation without the need of human labels. We present pre-training and transfer learning results on real-life sonar image datasets. Our results indicate that self-supervised pre-training yields classification performance comparable to supervised pre-training in a few-shot transfer learning setup across all three methods. Code and self-supervised pre-trained models are be available at https://github.com/agrija9/ssl-sonar-images
