In recent years, usage and applications of Autonomous Underwater Vehicles has grown rapidly. Interaction of divers with the AUVs remains an integral part of the usage of AUVs for various applications and makes building robust and efficient underwater gesture recognition systems extremely important. In this paper, we propose an Underwater Gesture Recognition system trained on the Cognitive Autonomous Diving Buddy Underwater gesture dataset using deep learning that achieves 98.01\% accuracy on the dataset, which to the best of our knowledge is the best performance achieved on this dataset at the time of writing this paper. We also improve the Gesture Recognition System Interpretability by using XAI techniques to visualize the model's predictions.

翻译：近年来，自主水下航行器的使用和应用迅速增长。潜水员与自主水下航行器的交互仍是其各种应用中的关键环节，这使得构建稳健高效的水下手势识别系统变得至关重要。本文提出了一种基于深度学习的水下手势识别系统，该系统利用认知自主潜水伙伴水下手势数据集进行训练，在数据集上达到了98.01%的准确率，据我们所知，这是撰写本文时在该数据集上取得的最佳性能。此外，我们通过运用可解释人工智能技术可视化模型的预测结果，提升了手势识别系统的可解释性。