Ammunition Component Classification Using Deep Learning

TL;DR

This paper presents a deep learning approach using YOLOv4 to automatically classify ammunition scrap as safe or unsafe based on visual and x-ray images, improving safety and efficiency in recycling processes.

Contribution

The study introduces a novel dataset and applies YOLOv4 for ammunition component detection and classification, demonstrating feasibility with augmented data and providing open access to datasets and models.

Findings

Deep learning effectively classifies ammo scrap safety.

Data augmentation improves model performance.

Models achieve accurate detection on unseen data.

Abstract

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.