Sabaragamuwa University of Sri Lanka

DEEP LEARNING BASED ARCHAEOLOGICAL OBJECT DETECTION FROM DRONE IMAGERY

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dc.contributor.author I.T.P., Jayaratne
dc.contributor.author S., Koswatte
dc.date.accessioned 2023-03-16T04:13:00Z
dc.date.available 2023-03-16T04:13:00Z
dc.date.issued 2022-12-01
dc.identifier.issn 2961-5895
dc.identifier.uri http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/3482
dc.description.abstract The recent technological advancements in the field of geoinformatics has enabled faster surveying techniques that are emerging in the field of archaeology. However, carrying out such surveys in faster means while preserving the original accuracies and reliability which are the properties of traditional archaeological surveys is still challenging. The lower costs, least possible manpower, and a minimal disturbance to the archaeological sites during the survey are also among the general expectations of archaeological surveys. The drone technology, image processing software and cloud-based spatial platforms with analysis capabilities can combinedly assist for achieving the above objectives. This research developed a semi-automated archaeological object detection algorithm which can extract archaeological objects from drone images. The study area of this research was the Ramba Raja Maha Viharaya Archaeological Monastery site situated in Hambantota, Sri Lanka. A series of drone images were acquired using DJI Phantom 4 RTK drone and 20 MP, 1-inch CMOS sensor. The acquired images were processed using image processing functions and object detection and extraction algorithms written in Python language. The results and the accuracy verifications depict that the process of extracting archaeological ruins from the drone images was successful and in an acceptable accuracy. The confusion matrix returned in the model training was used to calculate the accuracy of the model since the raw accuracy is not very reliable when measuring the performance of a neural network. The performance indicators of the confusion matrix, that is the precision and recall were 61.7% and 95.5% respectively. en_US
dc.language.iso en en_US
dc.publisher Faculty of Geomatics Sabaragamuwa University of Sri Lanka en_US
dc.subject Archaeological Survey en_US
dc.subject Automated Object Detection en_US
dc.subject Drone Imagery en_US
dc.subject Python Deep Learning en_US
dc.title DEEP LEARNING BASED ARCHAEOLOGICAL OBJECT DETECTION FROM DRONE IMAGERY en_US
dc.type Article en_US


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