Pregled bibliografske jedinice broj: 1274268
Material Classification of Underground Objects From GPR Recordings Using Deep Learning Approach
Material Classification of Underground Objects From GPR Recordings Using Deep Learning Approach // The Second Serbian International Conference on Applied Artificial Intelligence (SICAAI) - Book of Abstracts / Filipović, Nenad (ur.).
Kragujevac: University of Kragujevac, Serbia, 2023. str. /-/ (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1274268 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Material Classification of Underground Objects From
GPR Recordings Using Deep Learning Approach
Autori
Štifanić, Daniel ; Štifanić, Jelena ; Baressi Šegota, Sandi ; Anđelić, Nikola ; Car, Zlatan
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
The Second Serbian International Conference on Applied Artificial Intelligence (SICAAI) - Book of Abstracts
/ Filipović, Nenad - Kragujevac : University of Kragujevac, Serbia, 2023, /-/
ISBN
978-86-81037-77-5
Skup
The Second Serbian International Conference on Applied Artificial Intelligence (SICAAI)
Mjesto i datum
Kragujevac, Srbija, 19.05.2023. - 20.05.2023
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
ground penetrating radar ; artificial intelligence ; deep learning ; convolutional neural network ; classification
Sažetak
Exploration and detection of underground objects without excavation can be achieved by utilizing ground penetrating radar. Since such an approach is nondestructive, electromagnetic radiation has been used in order to accomplish sub-surface surveying. The correct interpretation of acquired ground penetrating radar data can be demanding, time-consuming and very challenging especially when the observed environment is noisy. However, with the assistance of artificial intelligence algorithms, such data can be processed and analyzed at high speed and with high accuracy. The aim of this research was to develop a deep learning model for the material classification of underground objects from ground penetrating radar recordings. Within the recordings, the pipes are usually visually represented as hyperbola-shaped features of different characteristics. Annotated by experts and preprocessed ground penetrating radar recordings were used as input to the deep convolutional neural networks. After the model was fully trained, performance on the validation set showed that the developed model can be used for pipe material classification from ground penetrating radar recordings with satisfactory results.
Izvorni jezik
Engleski
Znanstvena područja
Interdisciplinarne tehničke znanosti
POVEZANOST RADA
Projekti:
KK.01.1.1.01.0009 - Napredne metode i tehnologije u znanosti o podatcima i kooperativnim sustavima (EK )
--KK.01.2.2.03.0004 - Centar kompetencija za pametne gradove (CEKOM) (Car, Zlatan; Slavić, Nataša; Vilke, Siniša) ( CroRIS)
InoUstZnVO-CIII-HR-0108-10 - Concurrent Product and Technology Development - Teaching, Research and Implementation of Joint Programs Oriented in Production and Industrial Engineering (Car, Zlatan, InoUstZnVO - CEEPUS) ( CroRIS)
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-275-1447 - Razvoj inteligentnog ekspertnog sustava za online diagnostiku raka mokračnog mjehura (Car, Zlatan, NadSve - UNIRI potpore) ( CroRIS)
--uniri-mladi-technic-22-61 - Energetska optimizacija industrijskih robotskih manipulatora primjenom algoritama evolucijskog računarstva (Anđelić, Nikola) ( CroRIS)
Ustanove:
Tehnički fakultet, Rijeka
Profili:
Zlatan Car (autor)
Jelena Musulin (autor)
Nikola Anđelić (autor)
Sandi Baressi Šegota (autor)
Daniel Štifanić (autor)