Landmines cause enormous humanitarian and economic problems around the world. This document proposes using image processing techniques to detect landmines. It discusses using filters like median and weighted median filters to reduce noise in images captured by sensor devices. A thresholding method is also introduced to convert images to binary to aid in landmine detection. The goal is to develop a safe and efficient landmine detection system using these image processing methods.

1. [May 2012]
Land Mine Detection and Image Processing
By
Ankush Srivastava
[Email: anksrizzz@gmail.com, anksri000@gmail.com]
Abstract: Landmines are causing
enormous humanitarian and Introduction: Landmines are causing
economic problems in many countries enormous problems in a large number
all over the world. Experts estimate of areas throughout the world today
that up to 110 million landmines need [1]. Landmines are a significant cause
to be cleared and more than 20,000 of suffering in many developing
civilians are killed or maimed every nations. They pose a great threat to
year by landmines, with many of the individuals for years after conflict has
victims being children [11]. However, ceased and can be a serious
landmine detection and clearance impediment to industrial and
have turned out to be an extremely agricultural development [8]. There
challenging problem. At the current are more than 100 million mines in
clearance rate, it will take about 1,000 more than 70 countries [7]. As per as
years to remove all landmines that are their purpose, mines can be classified
already placed and for every landmine into three types, antipersonnel mine
cleared, further 20 are being buried. (APM), antitank mine (ATM), and
Therefore it is urgent to develop a unexploded ordnance (UXO).
safe and cost efficient landmine
detection system. In the past fifteen
years, various techniques, including
acoustic sensor, infrared technique,
image processing techniques have
been investigated [10]. Landmine
detection with passive infrared
images can depend quite heavily on
the environmental conditions, and
there are cross over periods when the
thermal contrast is negligible and the
mines may be undetectable. This
work we deal with land mine
detecting through image processing.
Valuable information can be hidden in
the images.

2. Type APN ATM UXO
Landmine-detection: Traditionally Weight 100g~4 6kg~11 Various
there are two methods for detecting kg kg
hidden landmines: prodding and Size 6~15cm 13~40c Various
remote sensing. In prodding, a probe (diamete m
is gently inserted into soil to examine r)
the existence of a buried object. Target Human Vehicle None
intentiona
Remote sensing is the other
l, but can
methodology in which the presence of be any
an unexpected object on or thing
underneath the surface is examined Case Plastic Plastic, Mostly
using sensors such as electromagnetic Material Metal metal
induction sensors (EMI), X ray Detonata 500g 120kg Unpredict
backscatter radiography, ground ble able
Pressure
penetrating radar (GPR), infrared
cameras (IR), and thermal neutron Image
analyzers [4].
It is important to understand the
extent to which the design of mine [12]. Signal processing for landmine
detection and minefield delineation detection seeks to exploit
technology is based on military discrimination information in
operational doctrine, compared to measured signals from a variety of
humanitarian or post-conflict sensors. Signals used in mine
requirements [3]. detection are generally
Signal processing is a necessary, multidimensional [9]. The goal of this
fundamental component of all work is to detect and locate land
detection systems and can result in mines on the earth surface using
orders of magnitude improvement in image processing. This mechanism
the probability of detection and transfers low level image
reduce the false alarm rate of almost characteristics into high level
any sensor system. semantic features using image
processing algorithms.
Landmine-detection and Image
Processing: Image plays vital role in Landmine-detection concept:-
every aspect of business such as • Color and intensity analysis:
business images, satellite images, Detection is based on object color or
medical images and so on. If we intensity contrast with the
analysis these data, which can reveal surrounding background. The
useful information to the human users contrast threshold is defined by local

3. image statistics (mean and standard
deviation) of the image [2].
• Edge detection and grouping:
Straight or partly circular edges are
extracted because they can indicate
artificial objects. The subsequent step
groups edges into hypothetical
artificial objects [2].
• Polarization analysis: Objects are
detected based on their polarization Histogram
contrast with the surrounding
Median Filter: In median filtering,
background [2].
first we sort the surrounding pixels of
Regardless of the type of sensor, noise desired pixel behalf of its intensity
is always present [7]. For reducing value then desired pixel will be
these types of noise we use various replaced by middle element of sorted
types of filters. pixel values [5][6].
Some neighborhood operations work
with the values of the image pixels in
the neighborhood and the
corresponding values of a sub image
that has the same dimensions as the
neighborhood. The sub image is called
a filter [5].
In this work we introduce two
filtering methods; Median Filter and Applying Median Algorithm
Weight Median Filter; for removing
the noise.
Histogram
Original image received from sensor devices Weight Median Filter: In weight
median filter, first we create weight

4. matrix then put values of surrounding
(of noisy pixel) pixels in single dim
array with the repetitive values
according to the values of weight
matrix and sort it after that noisy
pixel is replays by the middle element
of the sorted array which full of pixel
values [6].
Original image received from sensor devices
Applying Weight Median Algorithm
Threshold image; where threshold value is
170.
Sensor Devices
Images
Image threshold Noise Reduction
Histogram
Result
Threshold Method: In this section
we introduce a new mechanism for
detecting the landmine. In this User
mechanism, we simply converting the
Gary scale and color images into Block Diagram of the system
binary or black/white images with Conclusion: This paper is an outcome
specific threshold value or it may be of the study, that a landmine detection
vary. It is also known as image system which can be viewed as an
threshold. interactive system with a user
responsible to make queries to the

5. system, similar to a text search engine 6. Wilhelm Burger, Mark J. Burge,
but different in input method. We are “Principles of Digital Image
focuses on the image processing for Processing”.
detect the landmine on the earth 7. Cheolha Pedro Lee, “Mine
surface. Various types of sensor Detection Techniques Using
devices capture the images of the land Multiple Sensors”.
surface, and the noise is always 8. Peter Torrione, and Leslie M.
presents. For this purpose we applied Collins, “Texture Features For
two image processing algorithms that Anti-Tank Landmine Detection
is, Median Filter and Weight Median Using Ground Penetrating Radar”.
Filter, to reduce the noise. We also 9. Paul Gader, “SIGNAL-PROCESSING
introduce new technique for detecting AND SENSOR FUSION METHODS”.
the land mine that is image threshold 10. Yijun Sun and Jian Li, “Adaptive
method with the specific threshold Learning Approach to Landmine
value. Detection”.
11. “Adopt-a-minefield,
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