Unmanned Aircraft Systems (UASs) have been recognized as an important resource in search-and-rescue (SAR) missions and, as such, have been used by the Croatian Mountain Search and Rescue (CMRS) service for over seven years. The UAS scans and photographs the terrain. The high-resolution images are afterwards analyzed by SAR members to detect missing persons or to find some usable trace. It is a drawn out, tiresome process prone to human error. To facilitate and speed up mission image processing and increase detection accuracy, we have developed several image-processing algorithms. The latest are convolutional neural network (CNN)-based. CNNs were trained on a specially developed image database, named HERIDAL. Although these algorithms achieve excellent recall, the efficiency of the algorithm in actual SAR missions and its comparison with expert detection must be investigated. A series of mission simulations are planned and recorded for this purpose. They are processed and labelled by a developed algorithm. A web application was developed by which experts analyzed raw and processed mission images. The algorithm achieved better recall compared to an expert, but the experts achieved better accuracy when they analyzed images that were already processed and labelled.
Reliability program is considered to be a very valuable means of achieving better operational performance (through decreased maintenance related problems in operation) and increased flight safety. For this reason, reliability programs are mandated by the regulations for all commercial operators. Depending on the size of the operator, implementation of reliability program can be carried out in various organizational forms. Small fleets represent too small a statistical sample to collect enough information for obtaining statistically significant and accurate data. Therefore usability of reliability program in very small fleets is questionable. The aim of this work is to highlight some problems related to maintenance reliability program for small fleets.
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