Development of an Automatic Testing Platform for Aviator’s Night Vision Goggle Honeycomb Defect Inspection
- A novel searching algorithm, which is able to achieve fast and accurate focusing, is proposed. The main advantage of the developed method is also addressed through a comparison study.
- Different sharpness estimation methods for NVGs are also considered for comparison studies.
- A honeycomb defect detection process is proposed to automatic point out the number of defects and it corresponding locations. Therefore, the detection procedure can be realized efficiently and objectively.
2. Proposed Approach
2.1. A. Experimental Setup
2.2. Process for Passive Auto-Focusing
2.3. Search Approaches
2.4. Honeycomb Defect Detection Procedure
- A color image of dimensions 960 × 1280 × 3 pixels is obtained via a CCD camera, as shown in Figure 5a.
- Since the green color array component from the RGB color system has the most obvious honeycomb defects and the blue color array component has the least, the green array is subtracted from the blue array after extracting them from the color image to reduce processing dimensions. This allows a reduction in the non-honeycomb defect region components and accentuates the honeycomb defect region information. The obtained gray level image is shown in Figure 5b.
- The sum square difference (SSD) method  is used to detect the positions of the mark, which is applied to the focusing process, using the template image shown in Figure 5j. The mark area is highlighted by a rectangular region shown in Figure 5d. The matching measure SSD is defined as follows:
- 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. During the operation, a sub-image will be binarized using a threshold of its average intensity. If the amount of the corresponding pixels having the same value with “template 2” is greater than 55% of the total amount, the sub-image will set to be white. Otherwise, it will be black. The “white” area indicates possible location of the honeycomb defect as shown in Figure 5e. Hence, this step will produce a binary image, wherein the white points mark locations that are similar to honeycomb defects. The process of calculated possible defect location image is summarized in Table 3.
- White color expansion is performed on the resulting binarized image of step 5 to group the honeycomb defects around the neighborhood, as shown in Figure 5f.
- The region of mark identified in step 4 is removed for further processing. After the removal, the image becomes Figure 5g.
- For honeycomb defect positioning, the centroid of each white-colored dot indicates the upper left corner of a honeycomb defect. The result is shown in Figure 5h.
3. Results and Discussion
Conflicts of Interest
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|In-Focus Result||Average Elapsed Time (s)||Correlation Coefficient||Entropy|
|Absolute Central Moment||N||0.0009||−0.2280||0.5203|
|Brenner’s focus measure||Y||0.0012||−0.1168||0.5942|
|Image curvature measure||Y||0.0049||0.0980||2.9132|
|DCT energy ratio||Y||5.2191||0.1596||0.5203|
|DCT reduced energy ratio||Y||5.0998||0.1667||2.2880|
|Gray-level local variance||Y||0.0030||0.1500||0.7414|
|Normalized gray-level variance||Y||0.0007||0.3330||0.2980|
|Thresholded absolute gradient||Y||0.0013||−0.0181||3.1345|
|Helmli and Scherer’s mean||Y||0.0024||0.0800||4.0896|
|Energy of laplacian||Y||0.0013||−0.1166||1.8104|
|Variance of Laplacian||Y||0.0014||−0.0882||0.4463|
|Steerable filters-based measure||Y||0.0067||0.3288||0.4463|
|Spatial frequency measure||Y||0.0013||0.0164||5.5735|
|Sum of wavelet coefficients||N||0.0097||−0.2256||0.5942|
|Variance of wavelet coefficients||Y||0.0087||−0.15375||1.1801|
|Ratio of wavelet coefficients||Y||0.0210||−0.0285||2.9132|
(L1: ±0, L2: ±1, L3: ±2, Failure)
|Improvement Percentage (%)|
|Rule-based search (12-3-2-1)||39||L1||64.55|
|Rule-based search (12-4-3-2)||24||L2||78.18|
|Rule-based search (12-5-4-3)||19||L2||82.73|
|Rule-based search (12-6-5-4)||16||L3||85.45|
|Gradient-based variable step search (0.25, 60, 1)||9||L2||91.82|
|Gradient-based variable step search (0.26, 60, 1)||11||L2||90|
|Gradient-based variable step search (0.27, 60, 1)||8||L2||92.73|
|Gradient-based variable step search (0.28, 60, 1)||14||L1||87.27|
|Gradient-based variable step search (0.29, 60, 1)||8||L2||92.73|
|Gradient-based variable step search (0.30, 60, 1)||9||L2||91.82|
| Inputs: Figure 5c,k.|
Outputs: The possible defect location image.
|Sample Condition |
(Inspected by Experts)
|Sample Item||Amount of Honeycomb Defects |
Detected by the Algorithm
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Jian, B.-L.; Peng, C.-C. Development of an Automatic Testing Platform for Aviator’s Night Vision Goggle Honeycomb Defect Inspection. Sensors 2017, 17, 1403. https://doi.org/10.3390/s17061403
Jian B-L, Peng C-C. Development of an Automatic Testing Platform for Aviator’s Night Vision Goggle Honeycomb Defect Inspection. Sensors. 2017; 17(6):1403. https://doi.org/10.3390/s17061403Chicago/Turabian Style
Jian, Bo-Lin, and Chao-Chung Peng. 2017. "Development of an Automatic Testing Platform for Aviator’s Night Vision Goggle Honeycomb Defect Inspection" Sensors 17, no. 6: 1403. https://doi.org/10.3390/s17061403