Marine vessels undergo periodic inspections under which the condition of the vessel is documented. A part of these inspections is to detect defects such as corrosion that degrade the structural integrity of the vessel. The goal of this paper is to evaluate several deep learning architectures and create a hierarchical pipeline that best fits an autonomous inspection system, in the form of an unmanned aerial vehicle, capable of detecting defects in the ballast tanks of a marine vessel. Due to the limited resources available on such an autonomous system, we devised and tested a pipeline to use a smaller deep learning architecture to trigger a larger one when the presence of corrosion is detected. The produced segmentation can then be used to compute the condition of the vessel. In total ten architectures/combinations were tested ranging from traditional classification to object detection and instance segmentation. All the architectures were trained on a dataset containing images from ballast tanks with varying degree of corrosion. The results presented in this paper show that regular object localization architectures such as YOLO and Faster-RCNN suffer from overestimation of the affected corroded area. Binary whole image classification followed by instance segmentation proved to be the best performing pipeline.
Object detection has been in the focus of researchers within varying applications propelled by the recent advances in deep learning and neural networks. Many applications require both detection of class instances as well as a quantification of the spatial coverage of the class instances. While the performance of deep learning approaches for these tasks has been extensively studied there has not been much effort into creating a unified network structure to achieve both goals. The purpose of this paper is to present a regressor to the faster R‐CNN architecture that can help quantify the spatial coverage estimation of some detected object. The goal of the regressor is to provide a reproducible result of the spatial coverage. To demonstrate the developed architecture, an example use‐case of land cover estimation is used. The experiments conducted in this paper show that the network does not sacrifice object detection accuracy, and indicate that the network is able to estimate the spatial coverage of six different types of land.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.