Evaluating aerial vessel detector in multiple maritime surveillance scenarios

In this paper we present an autonomous detection approach for airborne surveillance in maritime scenarios. This approach is robust to sun glare, waves and scale variation. Additionally, we introduce a new metric to evaluate detection and tracking results that is more adequate for these scenarios. The proposed detection method is evaluated using videos from different monitoring missions and its results are compared with a state-of-the-art neural network. This comparison is done using a traditional and the proposed evaluation metric.