With the growing interest in underwater exploration and monitoring, Autonomous Underwater Vehicles (AUVs) have become essential. The recent interest in onboard Deep Learning (DL) has advanced real-time environmental interaction capabilities relying on efficient and accurate vision-based DL models. However, the predominant use of sonar in underwater environments, characterized by limited training data and inherent noise, poses challenges to model robustness. This autonomy improvement raises safety concerns for deploying such models during underwater operations, potentially leading to hazardous situations. This paper aims to provide the first comprehensive overview of sonar-based DL under the scope of robustness. It studies sonar-based DL perception task models, such as classification, object detection, segmentation, and SLAM. Furthermore, the paper systematizes sonar-based state-of-the-art datasets, simulators, and robustness methods such as neural network verification, out-of-distribution, and adversarial attacks. This paper highlights the lack of robustness in sonar-based DL research and suggests future research pathways, notably establishing a baseline sonar-based dataset and bridging the simulation-to-reality gap.

翻译：随着水下探索与监测需求的日益增长，自主水下航行器（AUVs）已成为不可或缺的工具。近年来，对机载深度学习（DL）的关注推动了依赖高效、准确的视觉深度学习模型的实时环境交互能力的发展。然而，水下环境中声纳的主导应用，以及其训练数据有限和固有噪声的特点，给模型的鲁棒性带来了挑战。这种自主性的提升引发了在水下作业中部署此类模型时的安全问题，可能导致危险情况。本文旨在首次在鲁棒性范畴内对基于声纳的深度学习进行全面综述。它研究了基于声纳的深度学习感知任务模型，如分类、目标检测、分割和SLAM。此外，本文系统梳理了基于声纳的最先进数据集、模拟器以及鲁棒性方法，如神经网络验证、分布外检测和对抗攻击。本文强调了当前基于声纳的深度学习研究中鲁棒性的不足，并提出了未来的研究方向，特别是建立基准声纳数据集以及弥合仿真与现实的差距。