Abstract—The purpose of this study was to analyse the
explosive forensic data contained in digital image. The explosive
forensic data used in this study was mainly targeted on the
cross-section image of steel rod shrapnel. Digital image
processing techniques were used to find the areas and types of
the shrapnel. The process began with imaging segmentation to
find the boundary box of the shrapnel area and then extracted
the areas which contained key features and computed the
statistical values of those areas, Finally, the statistical values
were administered to the Artificial Neural Network to classify
the types of shrapnel. The results showed 71% accuracy which
was acceptable since each type of cross-section image had a very
slightly different and much different to detect by human eyes.
Index Terms—Explosive ordnance disposal, neural network, shrapnel, steel rod.
Kittiya Poonsilp is with the Department of Computer Science, Suan Sunandha Rajabhat University, Bangkok, Thailand (e-mail: email@example.com).
Cite: Kittiya Poonsilp, "Imaging Analysis of Steel Rod Shrapnel Using Artificial Neural Networks," International Journal of Machine Learning and Computing vol. 10, no. 5, pp. 707-713, 2020.Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).