Using Deep Learning, Researchers Automate UAV-Based Land Mine Detection

Facebook X LinkedIn Email
BINGHAMTON, N.Y., May 29, 2020 — Researchers at Binghamton University, who previously developed a method for detecting “butterfly” land mines using low-cost, commercial drones equipped with multispectral and infrared cameras, are now focusing on automated detection of land mines using convolutional neural networks.

Nicknamed “butterfly” land mines for their small size and butterfly-like shape, PFM-1 land mines are extremely difficult to locate and clear due to their small size, low trigger mass, and a design that mostly excludes metal components, making them virtually invisible to metal detectors.

The researchers analyzed multispectral and thermal data sets collected by an automated unmanned aerial vehicle (UAV) survey system featuring scattered PFM-1-type land mines as test objects. To automate land mine detection, they relied on supervised learning algorithms using a faster regional-convolutional neural network (Faster R-CNN).

The RGB visible light Faster R-CNN demo yielded a 99.3% testing accuracy for a partially withheld testing set and a 71.5% testing accuracy for a completely withheld testing set. The researchers found that across multiple test environments, the use of cm-scale, accurate, georeferenced data sets paired with Faster R-CNN allowed for accurate automated detection of the test PFM-1 land mines. The researchers said that their detection and mapping techniques could be calibrated to other types of scatterable antipersonnel mines in future trials to aid demining initiatives. For example, they could be adapted to detect and map disturbed soil for improvised explosive devices (IEDs).

The researchers said that the use of CNN-based approaches to automate the detection and mapping of land mines is much faster than manually counting land mines from an orthoimage (an aerial image that has been geometrically corrected). Also, a CNN-based method is quantitative and reproducible, unlike ocular detection, which is subjective and prone to human error. CNN-based methods can be generalized to detect and map any objects with distinct sizes and shapes from any remotely sensed raster images.

“Rapid drone-assisted mapping and automated detection of scatterable minefields would assist in addressing the deadly legacy of widespread use of small scatterable land mines in recent armed conflicts,” professor Alek Nikulin said,  

The research was published in Remote Sensing ( 

Using advanced machine learning, drones could be used to detect dangerous ‘butterfly’ land mines in remote regions of post-conflict countries, according to research from Binghamton University, State University at New York. Courtesy of Binghamton University, State University at New York.

Published: May 2020
multispectral imaging
Multispectral imaging is a technique that involves capturing and analyzing images at multiple discrete spectral bands within the electromagnetic spectrum. Unlike hyperspectral imaging, which acquires data across a continuous range of wavelengths, multispectral imaging is characterized by capturing information at several specific, predefined bands. This allows for the extraction of spectral signatures and information from different parts of the spectrum. Key aspects of multispectral imaging...
machine learning
Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on the development of algorithms and statistical models that enable computers to improve their performance on a specific task through experience or training. Instead of being explicitly programmed to perform a task, a machine learning system learns from data and examples. The primary goal of machine learning is to develop models that can generalize patterns from data and make predictions or decisions without being...
deep learning
Deep learning is a subset of machine learning that involves the use of artificial neural networks to model and solve complex problems. The term "deep" in deep learning refers to the use of deep neural networks, which are neural networks with multiple layers (deep architectures). These networks, often called deep neural networks or deep neural architectures, have the ability to automatically learn hierarchical representations of data. Key concepts and components of deep learning include: ...
Research & TechnologyeducationAmericasBinghamton UniversityImagingLight SourcesOpticsSensors & Detectorsneural networksconvolutional neural networksunmanned aerial vehiclesdronescamerasmultispectral imaginginfrared cameraslandminesdefensemachine learningenvironmentthermal camerasthermal sensorsUAVdeep learning

We use cookies to improve user experience and analyze our website traffic as stated in our Privacy Policy. By using this website, you agree to the use of cookies unless you have disabled them.