  Ammunition scrap inspection is an essential step in the process of recycling
ammunition metal scrap. Most ammunition is composed of a number of components,
including case, primer, powder, and projectile. Ammo scrap containing
energetics is considered to be potentially dangerous and should be separated
before the recycling process. Manually inspecting each piece of scrap is
tedious and time-consuming. We have gathered a dataset of ammunition components
with the goal of applying artificial intelligence for classifying safe and
unsafe scrap pieces automatically. First, two training datasets are manually
created from visual and x-ray images of ammo. Second, the x-ray dataset is
augmented using the spatial transforms of histogram equalization, averaging,
sharpening, power law, and Gaussian blurring in order to compensate for the
lack of sufficient training data. Lastly, the representative YOLOv4 object
detection method is applied to detect the ammo components and classify the
scrap pieces into safe and unsafe classes, respectively. The trained models are
tested against unseen data in order to evaluate the performance of the applied
method. The experiments demonstrate the feasibility of ammo component detection
and classification using deep learning. The datasets and the pre-trained models
are available at https://github.com/hadi-ghnd/Scrap-Classification.
