From Event: SPIE Defense + Security, 2018
This paper discusses a new capability developed for and results from a field portable test set for Gen 2 and Gen 3 Image Intensifier (I2) tube-based Night Vision Goggles (NVG). A previous paper described the test set and the automated and semi-automated tests supported for NVGs including a Knife Edge MTF test to replace the operator's interpretation of the USAF 1951 resolution chart. The major improvement and innovation detailed in this paper is the use of image analysis algorithms to automate the characterization of spot defects of I² tubes with the same test set hardware previously presented. The original and still common Spot Defect Test requires the operator to look through the NVGs at target of concentric rings; compare the size of the defects to a chart and manually enter the results into a table based on the size and location of each defect; this is tedious and subjective. The prior semi-automated improvement captures and displays an image of the defects and the rings; allowing the operator determine the defects with less eyestrain; while electronically storing the image and the resulting table. The advanced Automated Spot Defect Test utilizes machine vision algorithms to determine the size and location of the defects, generates the result table automatically and then records the image and the results in a computer-generated report easily usable for verification. This is inherently a more repeatable process that ensures consistent spot detection independent of the operator. Results of across several NVGs will be presented.
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stephen Scopatz, Metehan Ozten, Gilles Aubry, and Guillaume Arquetoux, "Automated spot defect characterization in a field portable night vision goggle test set," Proc. SPIE 10625, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXIX, 1062509 (Presented at SPIE Defense + Security: April 17, 2018; Published: 23 May 2018); https://doi.org/10.1117/12.2304295.
