A Review on Deep Learning Application for Detection of Archaeological Structures

Abstract

Over the last few years, archaeologist have started to look at automated object detection for searching of potential historical sites, using object identification methods that includes neural network-based and non-neural network-based approaches. However, there is a scarcity of reviews on Convolutional Neural Networks (CNN) based Deep Learning (DL) models for object detection in the archaeological field. The purpose of this review is to examine existing research that has been implemented in the area of ancient structures object detection using Convolutional Neural Networks. Notably, CNN based object detection has the difficulty to draw a boundary box around the object and was implemented mainly for object classification. Various algorithms such as, the Region-based Convolutional Neural Network (R-CNN) and Mask Region-based Convolutional Neural Network (MR-CNN) was developed to solve this problem, yielding a more accurate, time-efficient, and bias-free deep learning model. This paper intends to provide a technical reference highlighting articles from Scopus, Web of Science, and IEEE Xplore databases pertaining to the usage of Convolutional Neural Network based techniques to detect structures and objects in the archaeological field.