Authors - Aniket Chatterjee, Anirban Dasgupta, Parvez Aziz Boruah, Raktim Acharjee Abstract - Underwater gesture detection is a well-known area of research in recent times that helps in communication between divers and Underwater Remotely Operated Vehicle (ROV). Hand gestures are commonly used in underwater environments as a straightforward and intuitive method for conveying commands or messages between divers and ROV. The ROV need to first detect and identify the human and then detect his/her hand and what type of gesture it is. However, the underwater environment has many challenges: turbulent waters can disrupt the ROV navigation and obstruct the capture of clear video footage, resulting in noisy images that complicates the accurate recognition of hand gestures. Besides that, the ROV must process visual data and respond quickly, especially in critical situations where quick decision making is required. This project work aims to optimize the ROV application program for improved real-time image processing and gesture recognition, that helps in effective communication even under challenging underwater conditions. Six different models have been explored including techniques like Channel Attention Mechanism and Spatial Attention. Our developed model(UnderwaterGestureNet) have shown better result with less number of parameters. This lightweight model is more efficient to deploy in embedded system of an ROV.
