Proof of principle for helmet-mounted display image quality tester Conference Paper uri icon


  • Helmet-mounted displays (HMDs) provide essential pilotage and fire control imagery information for pilots. To maintain system integrity and readiness, there is a need to develop an image quality-testing tool for HMDs. There is currently no such tool. A framework for development of an image quality tester for the Integrated Helmet and Display Sighting System (IHADSS) used in the U.S. Army's AH-64 was proposed in Hsieh et al.. This paper presents the prototype development, summarizes the bench test findings using three IHADSS helmet display units (HDUs) and concludes with recommendations for future directions. The prototype tester consisted of hardware (two cameras, position sensors, image capture/data acquisition cards, battery pack, HDU holder, moveable rack and handle, and computer) and software algorithms for image capture and analysis. Two cameras with different apertures were mounted in parallel on a rack facing the HDU holder. A handle was designed to allow users to position the HDU in front of the two cameras. The HMD test pattern was then captured. Sensors were used to detect the position of the holder and whether the HDU was angled correctly in relation to the camera. Two sets of unified algorithms were designed to detect image features presented by the two cameras. These features included focus, orientation, displacement, field-of-view (FOV), and number of gray-shades. Images of test patterns were captured, analyzed and used to develop a specification for each inspection feature. Experiments were conducted to verify the robustness of the algorithms. Worst-case scenarios for factors such as clockwise and counterclockwise tilt, degree of focus, magnitude of brightness and contrast, and shifted images were created and evaluated. Bench testing of three field-quality HDUs indicated that the image analysis algorithms are robust and able to detect the desired image features. Suggested future work includes development of a learning algorithm to automatically develop or revise feature specifications as the number of inspection samples increases.

name of conference

  • Helmet- and Head-Mounted Displays VIII: Technologies and Applications

published proceedings


author list (cited authors)

  • Hsieh, S. J., Harding, T. H., Rash, C. E., Beasley, H. H., & Martin, J. S.

citation count

  • 3

complete list of authors

  • Hsieh, SJ||Harding, TH||Rash, CE||Beasley, HH||Martin, JS

editor list (cited editors)

  • Rash, C. E., & Reese, C. E.

publication date

  • September 2003