Real-time Multibeam Echosounder error detection using deep learning
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Date
2024-08
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University of New Brunswick
Abstract
The use of Uncrewed Surface Vessels (USVs) for marine surveying is increasing due to technological advancements, but they face logistical constraints of power and space availability. The autonomous nature of these systems would benefit from real-time detection of data errors using AI to enhance surveying capabilities. However, consideration for installing new devices capable of using deep learning algorithms on an USV must account for these constraints. Image segmentation is widely used in medicine to detect brain tumours and autonomous driving, and could be applied using the U-Net architecture to predict possible errors in real-time from USV Multibeam Echosounders. Tools like the Nvidia Jetson Orin AGX can facilitate real-time processing and analysis of data, while not impacting the operational efficiency of the USV. Integrating deep learning with USV operations shows promise in effectively identifying data errors, improving the automation of marine surveying, and simplifying data analysis.