Abstract
Due to the direct influence of night vision equipment availability on the safety of night-time aerial reconnaissance, maintenance needs to be carried out regularly. Unfortunately, some defects are not easy to observe or are not even detectable by human eyes. As a consequence, this study proposed a novel automatic defect detection system for aviator’s night vision imaging systems AN/AVS-6(V)1 and AN/AVS-6(V)2. An auto-focusing process consisting of a sharpness calculation and a gradient-based variable step search method is applied to achieve an automatic detection system for honeycomb defects. This work also developed a test platform for sharpness measurement. It demonstrates that the honeycomb defects can be precisely recognized and the number of the defects can also be determined automatically during the inspection. Most importantly, the proposed approach significantly reduces the time consumption, as well as human assessment error during the night vision goggle inspection procedures.
Highlights
Night vision goggles (NVGs) are used to enhance the visibility of helicopter crew members in low-light environments [1]
The basic NVGs structure is comprised of a mounting frame to hold all of the components, an objective lens to focus the night image onto the photocathode, a channel-plate proximity-focused image-intensifier, and a magnifying eyepiece with focusing adjustments to display the intensified image to the viewer [2]
To find the location of the honeycomb defects, the binary template, Figure 5k, of size 61 × 58 pixels is introduced to carry out a “shift” operation for the image obtained in step 3
Summary
Night vision goggles (NVGs) are used to enhance the visibility of helicopter crew members in low-light environments [1]. Image peculiarities commonly seen in NVGs include shading, edge glow, bright spots, dark spots, honeycomb, distortion, flicker, and scintillation [5] Among these peculiarities, the honeycomb defect is known as fixed pattern noise of a faint hexagonal form. To reduce the technical training time and to achieve efficient inspection of the NVGs, a camera was installed on the testing platform which captured the image through the NVGs. An auto-focusing algorithm and custom-design hardware were integrated to develop an automated defect detection system for NVGs. Automatic detection of honeycomb defects using image pattern recognition was proposed to reduce the calibration difficulty of manual operations. This study adjusted focal length using the images obtained from the lens of the NVGs. a passive auto-focusing method was used. The detection procedure can be realized efficiently and objectively
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