Ricerc@Sapienza

Maritime Situational Awareness has recently become a hot topic in the research community. The marine industry is engaged in the protection of vessels from numerous threats, underwater object avoidance and navigation support in dangerous waters. The growing demands of marine safety application require that the technical level of electronic equipment for the vessels be upgraded. In this context, underwater drones as Autonomous Underwater Vehicles (AUVs) can help to understand marine and other environmental issues and protect the ocean and vessels areas. AUVs are equipped with a lot of sensors to transmit data in order to perform monitoring tasks. One of the most important sensor is the Forward-Looking Sonars (FLS) that is generally used for navigation safety purposes on large ships and vessels. In order to act promptly, the common marine security intervention requires a real-time feedback from FLS sensor, which is quite a challenge for cable less systems such as AUVs due to the bandwidth limitation of underwater acoustic channels. Additionally, the FLS images suffer the presence of interactions among visual cues and artifacts, so that processing steps are needed. This project answers these challenges by providing an image processing and compression FLS image for fully automated coding and decoding strategy able to detect the relevant information and reducing the amount of data to transmit.