Extending electronic shot counters with machine learning-based ammunition discrimination for targeted maintenance

Aug 4, 2026

Weapon fleets degrade differently depending on usage, creating high maintenance cost and reliability risks for users and mission success. Electronic shot counters can improve tracking and enable more efficient preventive maintenance. The paper proposes a machine learning approach for generic ammunition-type discrimination using weakly labelled data based only on total shot counts. It adapts the EDGAR technique via intermediate neural activations and evaluates against live/blank discrimination. Results show a 94% instance-level error-rate improvement and perfect burst-firing performance, generalizing across platforms.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

AccessFree to download

Introduction

Operational impact of electronic shot counters

Related work and sensing approaches

Magnetic sensor solutions

MEMS accelerometer solutions

Proposed machine learning method

The current issue and full text archive of this journal is available on Emerald Insight at: [https://www.emerald.com/insight/2399-6439.htm](https://www.emerald.com/insight/2399-6439.htm) Extending electronic shot counters with machine learning-based ammunition discrimination for targeted maintenance Nathan Morsa Department ofElectrical Engineering and Computer Science, University ofLige, Lige, Belgium and FN e-novation, FN Herstal, Herstal, Belgium Abstract Purpose–Weapon fleets degrade differently depending on usage. Electronic shot counters provide armorers with accurate tracking and allow more efficient preventive maintenance practices. We propose a machine learning technique for general-purpose ammunition type discrimination, thereby enhancing targeted maintenance. Design/methodology/approach–We study an experimental shot counter deployment to understand its impact on maintenance practices. We then extend the existing EDGAR machine learning technique to solve the discrimination problem in a generic way on a weakly-labelled dataset, requiring only the total counts for each shot type. By repurposing intermediate neural network activations, we simplify training and minimize computational overhead. We evaluate our approach against a widely used live/blank discrimination algorithm. Findings–We show a 94% improvement in instance-level error rate and perfect burst-firing performance. This generalizes across weapon platforms without hyperparameter adjustment. The feature incurs as little as an 8% overhead (2.8 ms on a 64 MHz ARM Cortex-M4F) . Originality/value – Compared to existing techniques, it promises applicability to a broader range of weapon configuration discrimination tasks, including platforms previously deemed too complex or constrained. Additionally, we examine how performance scales with dataset size to offer practical data collection guidelines, a major challenge in this field. This technique supports a new generation of shot counters and targeted maintenance, thereby reducing costs, preventing incidents and increasing operational availability. Keywords Electronic shot counters, Gunshot detection, Ammunition discrimination, Preventive maintenance, Operational availability, Weakly supervised learning Paper type Research paper 1. Introduction The acquisition and ongoing maintenance of small arms, along with the procurement of spare parts and accessories, represent substantial investments for modern armies. The management and upkeep of this weapon fleet demands considerable time and directly impacts the reliability of weapons, a factor critical to mission success and the safety of the user. Paradoxically, small arms management is one of the rare areas of military logistics that has not yet entered the digital era. Current tracking relies on manual paper logs and spreadsheets, leading to frequent errors that compromise maintenance schedules. One solution to this problem is the systematic use of electronic shot counters. These devices continuously and accurately track weapon usage and maintenance status. © Nathan Morsa. Published in the Journal of Defense Analytics and Logistics. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at [http://creativecommons.org/licences/by/4.0/](http://creativecommons.org/licences/by/4.0/)[ ](http://creativecommons.org/licences/by/4.0/)legalcode This work was supported by FN Herstal, which provided funding, weapons, dedicated time for the research and facilitated data collection. FN Herstal had no influence on study design, data analysis, interpretation of the results or the decision to publish. Journal of Defense Analytics and Logistics

FAQs

Why are electronic shot counters important for weapon maintenance?

They continuously and accurately track weapon usage and maintenance status, replacing error-prone manual logs and improving preventive maintenance scheduling reliability.

How does the proposed machine learning method discriminate ammunition types?

It extends the EDGAR technique to learn from weakly labelled data using only total counts for each shot type, repurposing intermediate neural network activations to reduce training burden.

What performance gains does the method achieve and does it generalize?

The approach improves instance-level error rate by 94% and achieves perfect burst-firing performance, generalizing across weapon platforms without hyperparameter adjustment.

Aug 4, 2026

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