Improving the Model for Person Detection in Aerial Image Sequences Using the Displacement Vector: A Search and Rescue Scenario

Recent results in person detection using deep learning methods applied to aerial images gathered by Unmanned Aerial Vehicles (UAVs) have demonstrated the applicability of this approach in scenarios such as Search and Rescue (SAR) operations. In this paper, the continuation of our previous research is presented. The main goal is to further improve detection results, especially in terms of reducing the number of false positive detections and consequently increasing the precision value. We present a new approach that, as input to the multimodel neural network architecture, uses sequences of consecutive images instead of only one static image. Since successive images overlap, the same object of interest needs to be detected in more than one image. The correlation between successive images was calculated, and detected regions in one image were translated to other images based on the displacement vector. The assumption is that an object detected in more than one image has a higher probability of being a true positive detection because it is unlikely that the detection model will find the same false positive detections in multiple images. Based on this information, three different algorithms for rejecting detections and adding detections from one image to other images in the sequence are proposed. All of them achieved precision value about 80% which is increased by almost 20% compared to the current state-of-the-art methods.

Health conditions of migrants, refugees and asylum seekers on search and rescue vessels on the central Mediterranean Sea, 2016–2019: a retrospective analysis

ObjectivesThis study will contribute to the systematic epidemiological description of morbidities among migrants, refugees and asylum seekers when crossing the Mediterranean Sea.SettingSince 2015, Médecins sans Frontières (MSF) has conducted search and rescue activities on the Mediterranean Sea to save lives, provide medical services, to witness and to speak out.ParticipantsBetween November 2016 and December 2019, MSF rescued 22 966 migrants, refugees and asylum seekers.Primary and secondary outcome measuresWe conducted retrospective data analysis of data collected between January 2016 and December 2019 as part of routine monitoring of the MSF’s healthcare services for migrants, refugees and asylum seekers on two search and rescue vessels.ResultsMSF conducted 12 438 outpatient consultations and 853 sexual and reproductive health consultations (24.9% of female population, 853/3420) and documented 287 consultations for sexual and gender-based violence (SGBV). The most frequently diagnosed health conditions among children aged 5 years or older and adults were skin conditions (30.6%, 5475/17 869), motion sickness (28.6%, 5116/17 869), headache (15.4%, 2 748/17 869) and acute injuries (5.7%, 1013/17 869). Of acute injuries, 44.7% were non-violence-related injuries (453/1013), 30.1% were fuel burns (297/1013) and 25.4% were violence-related injuries (257/1013).ConclusionThe limited testing and diagnostics capacity of the outpatient department, space limitations, stigma and the generally short length of stay of migrants, refugees and asylum seekers on the ships have likely led to an underestimation of morbidities, including mental health conditions and SGBV. The main diagnoses on board were directly related to journey on land and sea and stay in Libya. We conclude that this population may be relatively young and healthy but displays significant journey-related illnesses and includes migrants, refugees and asylum seekers who have suffered significant violence during their transit and need urgent access to essential services and protection in a place of safety on land.

Heroes or criminals? The legitimacy of humanitarian organizations rescuing lives in the Central Mediterranean Sea after 2017

Since late 2016, NGOs operating in the Mediterranean have been at the centre of a campaign of delegitimization and criminalization culminating with former Italian Interior Minister Matteo Salvini’s 2018 NGO ban, which de facto erased the presence of humanitarian search and rescue operations and left the national coastguards to deal with an unprecedented migration crisis. Drawing upon discourse analysis of Italian and international news media articles and informed by semi-structured interviews with NGO representatives, this study investigates the implications of such media-driven public hostility. The results are threefold: first, the climate of suspicion surrounding NGOs has damaged them profoundly and led to a dramatic increase in deaths. Consequently, second, NGO’s ability to present themselves publicly as legitimate has been heavily limited. Last, it is fundamental to investigate the range of legitimation strategies all organizations can use when victims of a media-led scandal or a smear campaign are legitimized by political institutions.

Analysis of accidents involved in touring/ sport/ recreational vessels and yachts in the Turkish search and rescue area

Our country is surrounded by seas on three sides; it is in an attractive position in terms of tourism due to its suitable climate and many history and cultures. Especially the stagnant structure of the Aegean and the Mediterranean allows sea tourism in almost four seasons; it is one of the routes preferred especially by yacht ships. In this respect, a maritime traffic arises due to touring, sports and recreational ships and yachts, and these ships occasionally cause accidents. In the study, marine accidents in the Turkish Search and Rescue Area involving touring, sports and recreational vessels and yachts are statistically analyzed. In this context, frequency distributions are carried out first and the hypotheses are examined by Chi-Square independence analysis.

A GRASP-Based Approach for Planning UAV-Assisted Search and Rescue Missions

Search and Rescue (SAR) missions aim to search and provide first aid to persons in distress or danger. Due to the urgency of these situations, it is important to possess a system able to take fast action and effectively and efficiently utilise the available resources to conduct the mission. In addition, the potential complexity of the search such as the ruggedness of terrain or large size of the search region should be considered. Such issues can be tackled by using Unmanned Aerial Vehicles (UAVs) equipped with optical sensors. This can ensure the efficiency in terms of speed, coverage and flexibility required to conduct this type of time-sensitive missions. This paper centres on designing a fast solution approach for planning UAV-assisted SAR missions. The challenge is to cover an area where targets (people in distress after a hurricane or earthquake, lost vessels in sea, missing persons in mountainous area, etc.) can be potentially found with a variable likelihood. The search area is modelled using a scoring map to support the choice of the search sub-areas, where the scores represent the likelihood of finding a target. The goal of this paper is to propose a heuristic approach to automate the search process using scarce heterogeneous resources in the most efficient manner.