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Marçal, D., Câmara, A., Oliveira, J. & de Almeida, A. (2024). Evaluating R-CNN and YOLO V8 for Megalithic Monument Detection in Satellite Images. In Computational Science – ICCS 2024. (pp. 162-170).: Springer.

D. A. Marçal et al.,  "Evaluating R-CNN and YOLO V8 for Megalithic Monument Detection in Satellite Images", in Computational Science – ICCS 2024, Springer, 2024, pp. 162-170

@incollection{marçal2024_1776104296009,
	author = "Marçal, D. and Câmara, A. and Oliveira, J. and de Almeida, A.",
	title = "Evaluating R-CNN and YOLO V8 for Megalithic Monument Detection in Satellite Images",
	chapter = "",
	booktitle = "Computational Science – ICCS 2024",
	year = "2024",
	volume = "",
	series = "",
	edition = "",
	pages = "162-162",
	publisher = "Springer",
	address = "",
	url = "https://link.springer.com/book/10.1007/978-3-031-63759-9"
}

TY  - CHAP
TI  - Evaluating R-CNN and YOLO V8 for Megalithic Monument Detection in Satellite Images
T2  - Computational Science – ICCS 2024
AU  - Marçal, D.
AU  - Câmara, A.
AU  - Oliveira, J.
AU  - de Almeida, A.
PY  - 2024
SP  - 162-170
DO  - 10.1007/978-3-031-63759-9_20
UR  - https://link.springer.com/book/10.1007/978-3-031-63759-9
AB  - 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.
ER  -