TY - JOUR
T1 - A survey of land mine detection technology
AU - Robledo, L.
AU - Carrasco, M.
AU - Mery, D.
N1 - Funding Information:
This work was supported in part by FONDEF – Chile under grant no. DO4I1334.
PY - 2009
Y1 - 2009
N2 - This paper describes the state of the art in land mine detection technology and algorithms. Landmine detection is a growing concern due to the danger of buried landmines to people's lives, economic growth and development. Most of the injured people have no connection with the reason why the mines were placed. There are 50-100 million landmines in more than 80 countries around the world. Deactivation is estimated at 100 000 mines per year, against the nearly 2 million mines laid annually. In this paper we describe and analyse sensor technology available including state-of-the-art technology such as ground penetrating radar (GPR), electromagnetic induction (EMI) and nuclear quadrupole resonance (NQR) among others. Robotics, data processing and algorithms are mentioned, considering support vectors, sensor fusion, neural networks, etc. Finally, we establish conclusions highlighting the need to improve not only the way images are acquired, but the way this information is processed and compared.
AB - This paper describes the state of the art in land mine detection technology and algorithms. Landmine detection is a growing concern due to the danger of buried landmines to people's lives, economic growth and development. Most of the injured people have no connection with the reason why the mines were placed. There are 50-100 million landmines in more than 80 countries around the world. Deactivation is estimated at 100 000 mines per year, against the nearly 2 million mines laid annually. In this paper we describe and analyse sensor technology available including state-of-the-art technology such as ground penetrating radar (GPR), electromagnetic induction (EMI) and nuclear quadrupole resonance (NQR) among others. Robotics, data processing and algorithms are mentioned, considering support vectors, sensor fusion, neural networks, etc. Finally, we establish conclusions highlighting the need to improve not only the way images are acquired, but the way this information is processed and compared.
UR - http://www.scopus.com/inward/record.url?scp=70449382613&partnerID=8YFLogxK
U2 - 10.1080/01431160802549435
DO - 10.1080/01431160802549435
M3 - Article
AN - SCOPUS:70449382613
SN - 0143-1161
VL - 30
SP - 2399
EP - 2410
JO - International Journal of Remote Sensing
JF - International Journal of Remote Sensing
IS - 9
ER -