DRONE-BASED CONTAINER CRANE INSPECTION: CONCEPT, CHALLENGES AND PRELIMINARY RESULTS
Keywords: Crane Inspection, Structural Health Monitoring, Drone, Defects, Database, Machine Learning
Abstract. Container crane inspection is a very important task to maintain their uninterrupted operation. Nevertheless, this is a costly and time-consuming activity if performed manually. Recently, image-based detection of surface damages or changes using drones has gained increasing interest in industry; especially when objects of interest have a complex structure like container cranes. One main aim of this paper is a single-epoch image analysis which will also serve later for multi-epoch processing. It provides reliable information about current defects that may lead to big damages if not inspected by experts. Naïve Bayes classifier is employed to classify the images in different classes of which critical defects and especially rust is important. The preliminary results show that the precision on the target class reached about 99%. However, 87% percent recall in this class is not enough and it should be improved for this application.
Having a large dataset requires an efficient data management system to provide users and decision makers with the information needed. In addition, in order to foster full automation, the aforementioned image analysis component should have a direct connection to the database and thus is able to query image and semantic information. We therefore introduce the second aim of our research, that is a concept for database design. Here, not only the raw data and the final results are integrated but also the intermediate results. At the same time, the database concept is connected to an integrated client interface that allows retrieving data of interest in a virtual globe.