Lipschitz Constrained Neural Networks for Robust Object Detection at Sea
Jonathan Becktor, Frederik Schöller, Evangelos Boukas, Mogens Blanke, Lazaros Nalpantidis · IOP Conference Series Materials Science and Engineering · 2020
Abstract Autonomous ships rely on sensory data to perceive objects of interest in their environment. Deep Learning based object detection in the image domain commonly used to solve this issue. The robustness of such approaches in non-ideal conditions is, however, still to be proven. In this work state of the art methods are applied on the RetinaNet architecture attempting to create a more robust object detection network given noisy input data. The GroupSort activation function and Spectral Normalization is used and the results are compared to the standard RetinaNet network. Our findings show that these modifications perform better and ensure robustness under moderate noise levels, than the standard RetinaNet network.