IPAB Seminar - 08/03/2018 - rescheduled

Title

Detection of oil spills and shipwrecked people using aerial sensor data and Deep Learning techniques

Abstract

According to information obtained from the Spanish Maritime Safety Agency (SASEMAR), maritime traffic spills more than 20 million m3/year of both oil and other hydrocarbons into the waters of the European Union. The presence of oil slicks on the sea surface requires early detection to activate emergency protocols focused on controlling the environmental impact and ecological damage to be carried out. Detection and monitoring are usually performed manually using two kinds of sensors: images in a visible spectrum and Synthetic Aperture Radar (SAR). Similar issues occur with the automatic detection of shipwrecked or drowned people. In Spain, small boats with immigrants arrive on the coast almost every day. Unfortunately, many of them are shipwrecked, and due to the waves and to the large area to explore, rescue missions are laborious and expensive. The maritime rescue service does not have any type of automatic system to help them in the search, therefore an application for this task is of great interest. In this project we use computer vision algorithms, specifically Convolutional Neural Networks, and information from different types of sensors such as color images, Side-Looking Airborne Radar (SLAR) or multispectral cameras, for the design of systems that automate these tasks or that help in their development.

Mar 08 2018 -

IPAB Seminar - 08/03/2018 - rescheduled

Antonio Javier Gallego Sánchez - University of Alicante - rescheduled

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