Headstamps are critical markings on ammunition that serve as a classification system, ensuring proper tracking of usage, transportation, storage, and quality control. Police departments worldwide rely on these identifiers to manage their armories effectively through dedicated ammunition control databases. Traditionally, officers performed inspection and cataloging tasks manually, which consumed significant time resources annually.

In Taiwan’s case, the National Police Agency (NPA) partnered with Nevis Technology—a reseller of The Imaging Source—to develop an advanced machine vision system incorporating deep-learning-based optical character recognition (OCR). This innovative solution automates ammunition inspection and classification. Project engineers selected specialized cameras from The Imaging Source specifically for this application.

Monochrome Industrial Cameras Enhance OCR Performance

The NPA had long sought automated solutions for inspecting ammunition but faced challenges in finding a reliable system until collaborating with Nevis Technology. Their vision-based approach proved highly effective, addressing the need for precision and efficiency.

Two high-quality monochrome cameras from The Imaging Source capture images of ammunition trays from multiple angles simultaneously. These specialized cameras deliver superior contrast-rich imagery ideal for OCR tasks, ensuring precise character localization and extraction despite varying conditions.

Deep Learning Powers Intelligent Quality Control

The system employs a sophisticated algorithm comparing extracted data against pre-trained datasets to identify anomalies or non-conforming items (marked in red). It processes one standard ammunition tray—typically containing 50 rounds—in just five seconds. Nevis Technology reports substantial benefits: approximately 60% reduction in manpower requirements and annual savings exceeding NT$2 million.

The OCR system utilizes deep learning for both defect detection and presence verification, further enhancing operational efficiency across police units handling ammunition management tasks.

Last Updated: 2025-09-05 00:16:50