Underwater Field Equipment of a Network of Landmarks Optimized for Automatic Detection by AI

To qualify the point clouds obtained by 3D reconstruction of a global study area in close-range remote sensing, control points, whose position has been measured essentially manually in the field with an instrument whose precision is known, are used. In the underwater environment, equipping the field and carrying out these measurements is a complex operation to perform due to the peculiarities of the environment. We present in this article a first step towards the automation of this task, the automatic detection of targets by a deep learning algorithm which will serve to correctly position the control points locally, and a simplification of the manual measurement which will serve in future work to control the results of automatic readings.