Abstract
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.
Highlights
Health monitoring is an essential process in ensuring the safety and serviceability of civil infrastructure like bridges and container cranes (Rao et al, 2020; Saleem et al, 2020; Stein, 2018)
Current practice for assessing structural health of container cranes is mainly based on visual inspections by human operators (Hoskere et al, 2020)
At HHLA’s Container Terminal Tollerort (CTT), where we implement and test our system, a total of 14 container gantry cranes of different years of construction from various manufacturers are in operation (Figure 8)
Summary
Health monitoring is an essential process in ensuring the safety and serviceability of civil infrastructure like bridges and container cranes (Rao et al, 2020; Saleem et al, 2020; Stein, 2018). A logical step forward in increasing the automation of container cranes’ inspection is employing the photos taken by drones and the visual evaluation of the photos with the help of qualified specialists This approach is still subjective and based on personal assessment, experience and the respective daily form of the operators. The volume of data will become larger over time so that a manual investigation will become increasingly difficult to perform in the terms of time, cost and capacity Another very important aspect of health monitoring of the container cranes is the temporal analysis of the captured images. The double-blind peer-review was conducted on the basis of the full paper
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