Artificial Intelligence-Based Low-Terahertz Imaging for Archaeological Shards' Classification

Description

In order to map the migration and introduction of farming into Europe during the 7th and 6th millennia Before Common Era, archaeologists have made a connection between the study of pottery and farming migration. We are interested here in the classification of pottery into coiling and spiral types, based on their manufacturing techniques. To distinguish between these two techniques, we look for the lines formed by air bubbles embedded in the pottery samples. Current methods make use of bulky systems, such as the Computerized Tomography scanners or synchrotrons. Microwave acquisition and processing offer an interesting alternative, due to the possibility to have compact and portable systems. In this paper, we investigate the classification of pottery based on Low-terahertz measurements in the D-band. We process the measurements with three-dimensional Fast Fourier Transform. The resulting matrix is classified with an Artificial Neural Network, the Multi-Layer Perceptron, which is optimized with the Grey Wolf Optimizer, a bio-inspired algorithm. The first results show that the accuracy reaches up to 99% using all the acquired spatial and frequency measurements. Then, we optimize the mm-Wave measurement system with a critical criterion on accuracy in two different scenarios. In the first scenario, we reduce the spatial acquisition but maintain the wideband operation and the results show that the accuracy is between 85% and 96%. In the second one, we reduce the spatial acquisition and use a single frequency. For this second scenario we achieve a classification accuracy which is between 77% and 100%.

Abstract

International audience

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