Estimating object depth using a vertical gradient metal detector

Jay Marble; Ian McMichael; Denis Reidy

doi:10.1117/12.779575

29 April 2008 Estimating object depth using a vertical gradient metal detector

Jay Marble, Ian McMichael, Denis Reidy

Proceedings Volume 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII; 695313 (2008) https://doi.org/10.1117/12.779575
Event: SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States

Abstract

Object depth is a simple characteristic that can indicate an object's type. Popular instruments like radar, metal detectors, and magnetometers are often used to detect the presence of a subsurface object. The next question is often, "How deep is it?" Determining the answer, however, is not as straight forward as might be expected. This paper explores the determination of depth using metal detectors. More specifically, it looks at a popular metal detector (the Geonics EM61) and makes use of its vertically separated coils to generate a depth estimate. Estimated depths are shown for UXO and small surface clutter from flush buried down to 48". Ultimately a statistical depth resolution is determined. An alternative approach is then considered that casts the depth determination problem as one of classification. Only two classes are considered important "deep" and "shallow". Results are shown that illustrate the utility of the classifier approach. The traditional estimator can provide a depth estimate of the object, but the classifier approach can distinguish between small shallow, large deep, and large shallow object classes.

Citation Download Citation

Jay Marble, Ian McMichael, and Denis Reidy "Estimating object depth using a vertical gradient metal detector", Proc. SPIE 6953, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XIII, 695313 (29 April 2008); https://doi.org/10.1117/12.779575

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available