Pregled bibliografske jedinice broj: 1243798
Object Depth Estimation From Line-Scan EMI Data Using Machine Learning
Object Depth Estimation From Line-Scan EMI Data Using Machine Learning // IEEE Sensors Conference 2022
Dallas (TX), Sjedinjene Američke Države: Institute of Electrical and Electronics Engineers (IEEE), 2022. str. 1-4 doi:10.1109/sensors52175.2022.9967098 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 1243798 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Object Depth Estimation From Line-Scan EMI Data
Using Machine Learning
Autori
Simic, Marko ; Ambrus, Davorin ; Bilas, Vedran
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
ISBN
978-1-6654-8465-7
Skup
IEEE Sensors Conference 2022
Mjesto i datum
Dallas (TX), Sjedinjene Američke Države, 30.10.2022. - 02.11.2022
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Depth estimation, metal detector, electromagnetic induction, electromagnetic tracking system, metallic object
Sažetak
In this paper, we present a novel approach to metallic object depth estimation using a pulse induction metal detector in combination with an electromagnetic tracking system. A dipole approximation model is used for modeling the spatial response of the metal detector, while 1D- convolutional neural network is employed for depth estimation. The proposed algorithm is experimentally validated in laboratory conditions. Given a single horizontal pass over a metallic object placed within the range (−10.5, −2.5) cm and (−1, 1) cm for the z and {; ; ; ; x, y}; ; ; ; coordinates, respectively, the algorithm estimates the depth of the object regardless of its shape, size, and material properties with a mean absolute error <4.5 mm .
Izvorni jezik
Engleski
