Abstract
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.
Subject
General Earth and Planetary Sciences
Reference29 articles.
1. Aliance for Drone Inovation, Fact and Figureshttp://www.droneinnovation.org/facts-and-figures.html
2. Integrating Search and Rescue Drones into Airspace—Case Study—Croatian Mountain Rescue Service;Biočić,2019
3. Two-stage segmentation of aerial images for search and rescue;Turić;Inf. Technol. Control,2010
4. Deep Learning Approach in Aerial Imagery for Supporting Land Search and Rescue Missions;Božić-Štulić;Int. J. Comput. Vis.,2019
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献