Use of convolutional networks for archaeological feature detection in geophysical surveys
Channel: Recording Archaeology
Date: 2020-11-16

In this paper, we will introduce the use of convolutional neural networks for feature detection in magnetostatic archaeological surveys. They consist of a small dataset of a hundred of high resolution geomagnetic surveys undertaken both in France (Briquetage de la Seille project, Lor-raine, National Museum Of Archaeology, Saint-Germain-en-Laye) and in Britain (Departement of Archaelogy, University of Oxford). Using a simple network architecture, we first constructed a classifier which detects archaeological targets in segmented (96x96px) images, and obtain a 85% percent accuracy with a relatively short training time on consumer grade GPU. We have then introduced the use of YOLO (You Only Look Once, Redmon et al. 2018), a fast and novel object detection technique on the same dataset. YOLO allowed us to not only detect archae-ological targets in the entire image, but to also draw accurate bounding boxes around them, providing us with reliable information about the number of objects within the survey and their size. Using YOLO, we obtained a high accuracy on the classes of objects detected (about 95%), although the accuracy was lower about position and size of the boxes. We conclude by providing further pathes to improve the system and we evaluate the relevance of Deep Learning in object in object in remote sensing data.


Martin Olivier