Towards automatic visual sea grass detection in underwater areas of ecological interest

In areas of ecological interest, the detection and control of seaweed such as Posidonia Oceanica is usually performed by divers. Due to the limited capacity of the scuba tanks and the human security protocols, this task involves several short immersions leading to poor temporal and spatial data resolution. Thus, it is desirable to automate this task by means of underwater robots. This paper describes a method to autonomously detect Posidonia Oceanica in the imagery gathered by an underwater robot. The proposed approach uses a set of Gabor filters to characterize an image. This characterization is used to detect the regions containing seaweed by means of a Support Vector Machine. The experiments, conducted with an Autonomous Underwater Robot in several marine areas of Mallorca, show promising results towards the automated seafloor classification from extended video sequences.