Availability of domain-specific datasets is an essential problem in object detection. Datasets of inshore and offshore maritime vessels are no exception, with a limited number of studies addressing maritime vessel detection on such datasets. For that reason, we collected a dataset consisting of images of maritime vessels taking into account different factors: background variation, atmospheric conditions, illumination, visible proportion, occlusion and scale variation. Vessel instances (including nine types of vessels), seamarks and miscellaneous floaters were precisely annotated: we employed a first round of labelling and we subsequently used the CSRT tracker to trace inconsistencies and relabel inadequate label instances. Moreover, we evaluated the out-of-the-box performance of four prevalent object detection algorithms (Faster R-CNN, R-FCN, SSD and EfficientDet). The algorithms were previously trained on the Microsoft COCO dataset. We compared their accuracy based on feature extractor and object size. Our experiments showed that Faster R-CNN with Inception-Resnet v2 outperforms the other algorithms, except in the large object category where EfficientDet surpasses the latter.

No abstract

In this paper, we describe an indoor positioning system designed to provide data on the movement patterns of hospital personnel. The data collection is ongoing and part of a larger study project, which aims to evaluate the impact of an information system implemented in a hospital setting. The indoor positioning system was designed to be non-intrusive and straightforward to deploy in multiple hospitals, while requiring minimal existing infrastructure. To this end, the system is based on battery-powered Bluetooth beacons, and mobile phones measuring the signal strength of the beacons to position their bearers. This paper describes the design and implementation of the system. We analyze the positioning accuracy of the system in order to evaluate its fitness for purpose. Based on the results, the system is suitable for short-term deployments due to its low cost and ease of installation.

No abstract

No abstract