Detection of objects on the ocean surface from a UAV with visual and thermal cameras: A machine learning approach

Unmanned aerial vehicles (UAVs) can provide great value in off-shore operations that require aerial surveillance, for example by detecting objects on the water surface. For efficient operations by autonomous aerial surveillance, a reliable automatic detection system must be in place: one that will limit the amount of false negatives, but not at the expense of too many false positives. In this paper, we assess multiple aspects of the detection system that may provide significant impact in off-shore aerial surveillance: First by assessing detection architectures based on convolutional neural networks, then by adding tracking algorithms to utilize temporal information, and finally by investigating the use of different imaging modalities. Through a comparison of several detection models, the experiments prove that misclassification of objects is a particular issue, where input resolution and size of objects influence the overall model performance. The use of a tracking algorithm allows for decreasing the confidence threshold, which results in fewer false negatives, without a significant increase in false positives. In addition, comparing information obtained from visual and thermal imaging systems shows that these modalities provide complementary information in the presence of sunlight reflection.