stručni rad
Deep learning has become widely utilized and extensively studied in vari-
ous fields such as autonomous vehicles and face recognition. However, its
application in domains like land mine (LM) clearance and explosive objects
clearance, particularly in non-military contexts, is relatively uncommon. This
article builds upon prior research that employed the YOLO algorithm for
detecting unexploded ordnance (UXO) in thermal images and extends it to
the near real-time detection of annotated explosive objects in a thermal video
sequence.
This work is based on the UXOTi_NPA dataset [1], which encompasses
11 distinct explosive targets, along with an original thermal video captured
from a height of 3, 5, and 7 meters with very high ground sampling distance.
YOLO, known for its speed and accuracy, demonstrated the capability to de-
tect explosive objects in over 40 frames per second (FPS), making it a viable
solution for thermal videos operating at 25 FPS or 30 FPS. Currently, no au-
tomated systems are available for surface UXO detection using thermal video
that can facilitate large-scale area surveys. Consequently, this research rep-
resents a significant step towards addressing this gap and paving the way for
such automated solutions. Visible RGB imaging, picture, and video alike are
thoroughly researched, proven by thousands of research results published on
object detection, classification, recognition, and identification using various
machine learning algorithms, so these are vast pools of possible solutions.
Less researched because of the lack of publicly available datasets and more
complex data processing are ground penetrating radar (GPR), magnetom-
eter, and hyperspectral imaging sensors and future solutions would seek an
application of deep learning algorithms for real-time detections with these
sensors and their fusion for higher probabilities of detection.
YOLO UXO thermal video
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