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Geo-referenced visual data for Posidonia Oceanica coverage using audio for data synchronization

机译:使用音频进行数据同步的Posidonia Oceanica Ruckage的地理参考视觉数据

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The supervision of underwater areas is essential for the preservation of marine ecosystems. In the Italian context, Professional divers of Environmental Protection Agencies are periodically involved in monitoring activities that are repetitive, dangerous, and expensive. The Ligurian Regional Agency for the Environmental Protection (ARPAL) and the University of Pisa (UNIPI) are collaborating towards the employment of robots and ICT tools to improve the monitoring activities in terms of safety, cost and time effectiveness. This paper reports a strategy to geo-reference underwater visual data using audio for data synchronization. The work refers to a visual dataset acquired by a Smart Dive Scooter (SDS) during a Posidonia Oceanica (Po) monitoring activity in front of the Ligurian Coast. The proposed strategy concerns the synchronization between the audio track recorded by a camera and the transponder pings adopted for the SDS acoustic positioning system. The paper also reports the exploitation of the geo-referenced optical data for the identification of Po meadow over the mission area using a Machine Learning algorithm. The results are very promising and can lead to an accurate geo-referenced identification of Po and the reconstruction of the surveyed area.
机译:水下地区的监督对于保护海洋生态系统至关重要。在意大利背景下,环境保护机构的专业潜水员定期参与监测重复,危险和昂贵的活动。环境保护(ARPAL)和比萨大学(UNIPI)的利古里亚区域机构正在合作机器人和ICT工具,以改善安全,成本和时间效率方面的监测活动。本文将策略向地理参考水下视觉数据报告使用音频进行数据同步。该工作是指在利古里亚海岸前面的Posidonia Oceanica(PO)监测活动期间由智能潜水踏板车(SDS)获取的视觉数据集。该策略涉及摄像机记录的音轨与SDS声学定位系统采用的应答器潮汐之间的同步。本文还报告了利用机器学习算法对Po Meadow识别的地理参考光学数据的利用。结果非常有前途,可导致准确的PO和重建调查区域的地理参考识别。

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