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Evaluating R-CNN and YOLO V8 for Megalithic Monument Detection in Satellite Images
Book Title
Computational Science – ICCS 2024
Year (definitive publication)
2024
Language
English
Country
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Abstract
Over recent years, archaeologists have started to use object detection
methods in satellite images to search for potential archaeological sites. Within
image object recognition, due to its ability to recognize objects with great accu-
racy, convolutional neural networks (CNN) are becoming increasingly popular.
This study compares the performance of existing deep-learning algorithms for the
detection of small megalithic monuments in satellite imagery, namely RCNN
(Region-based Convolutional Neural Networks) and YOLO (You Only Look
Once). Using a satellite image dataset and after adequate preprocessing, results
showed that this is a feasible approach for archaeological image prospection,
with RCNN achieving a remarkable precision of 93% in detecting these small
monuments.
Acknowledgements
This work was partially supported by the Fundação para a Ciência e a Tec-
nologia, I.P. (FCT) through the ISTAR-Iscte project UIDB/04466/2020 and UIDP/04466/2020,
through the scholarship UI/BD/151495/2021.
Keywords
object detection,satellite images,CNN,megalithic monuments,archaeology
Fields of Science and Technology Classification
- Computer and Information Sciences - Natural Sciences
- Other Engineering and Technology Sciences - Engineering and Technology
- Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology
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