Electromagnetic simulation model of microscopic scattering dark-field imaging for optical components surface defect detection

2020 ◽  
Author(s):  
Weimin Lou ◽  
Pin Cao ◽  
Haotian Hu ◽  
Zichen Lu ◽  
Yongying Yang
Keyword(s):  
Simulation Model ◽  
Defect Detection ◽  
Surface Defect ◽  
Dark Field ◽  
Electromagnetic Simulation ◽  
Optical Components ◽  
Dark Field Imaging ◽  
Surface Defect Detection ◽  
Field Imaging

Surface Defect Detection of Optical Components Based on Sparse Matrix

2019 ◽  
Vol 46 (4) ◽  
pp. 0404007
Author(s):  
陈晨 Chen Chen ◽  
王红军 Wang Hongjun ◽  
王大森 Wang Dasen ◽  
田爱玲 Tian Ailing ◽  
刘丙才 Liu Bingcai ◽  
...  
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Sparse Matrix ◽  
Optical Components ◽  
Surface Defect Detection

scholarly journals Surface Defect Detection on Optical Devices Based on Microscopic Dark-Field Scattering Imaging

2015 ◽  
Vol 61 (1) ◽  
pp. 24-32
Author(s):  
Yingjie Yin ◽  
De Xu ◽  
Zhengtao Zhang ◽  
Mingran Bai ◽  
Feng Zhang ◽  
...  
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Optical Devices ◽  
Dark Field ◽  
Surface Defect Detection

Detection of a point defect in a silicon single crystal by high-resolution TEM

1995 ◽  
Vol 53 ◽  
pp. 466-467
Author(s):  
M. Awaji
Keyword(s):  
High Resolution ◽  
Single Crystal ◽  
Point Defect ◽  
Point Defects ◽  
Noise Level ◽  
Dark Field ◽  
Silicon Single Crystal ◽  
High Resolution Image ◽  
Dark Field Imaging ◽  
Field Imaging

It is necessary to improve the resolution, brightness and signal-to-noise ratio(s/n) for the detection and identification of point defects in crystals. In order to observe point defects, multi-beam dark-field imaging is one of the useful methods. Though this method can improve resolution and brightness compared with dark-field imaging by diffuse scattering, the problem of s/n still exists. In order to improve the exposure time due to the low intensity of the dark-field image and the low resolution, we discuss in this paper the bright-field high-resolution image and the corresponding subtracted image with reference to a changing noise level, and examine the possibility for in-situ observation, identification and detection of the movement of a point defect produced in the early stage of damage process by high energy electron bombardment.The high-resolution image contrast of a silicon single crystal in the [10] orientation containing a triple divacancy cluster is calculated using the Cowley-Moodie dynamical theory and for a changing gaussian noise level. This divacancy model was deduced from experimental results obtained by electron spin resonance. The calculation condition was for the lMeV Berkeley ARM operated at 800KeV.

scholarly journals Noninvasive, in vivo assessment of the cervical microcirculation using incident dark field imaging

2021 ◽  
Vol 135 ◽  
pp. 104145
Author(s):  
Yani P. Latul ◽  
Arnoud W. Kastelein ◽  
Patricia W.T. Beemster ◽  
Nienke E. van Trommel ◽  
Can Ince ◽  
...  
Keyword(s):  
Dark Field ◽  
Dark Field Imaging ◽  
Incident Dark Field Imaging ◽  
Field Imaging ◽  
In Vivo Assessment

SDDNet: A Fast and Accurate Network for Surface Defect Detection

2021 ◽  
Vol 70 ◽  
pp. 1-13
Author(s):  
Lisha Cui ◽  
Xiaoheng Jiang ◽  
Mingliang Xu ◽  
Wanqing Li ◽  
Pei Lv ◽  
...  
Keyword(s):  
Defect Detection ◽  
Surface Defect ◽  
Surface Defect Detection

Lightweight detection algorithm for fine-grained surface defects of aerospace seal rings

2021 ◽  
pp. 1-18
Author(s):  
Hui Liu ◽  
Boxia He ◽  
Yong He ◽  
Xiaotian Tao
Keyword(s):  
Feature Extraction ◽  
Defect Detection ◽  
Surface Defect ◽  
Surface Defects ◽  
Detection Algorithm ◽  
Seal Ring ◽  
Fine Grained ◽  
Aerospace Applications ◽  
Surface Defect Detection ◽  
The Mean

The existing seal ring surface defect detection methods for aerospace applications have the problems of low detection efficiency, strong specificity, large fine-grained classification errors, and unstable detection results. Considering these problems, a fine-grained seal ring surface defect detection algorithm for aerospace applications is proposed. Based on analysis of the stacking process of standard convolution, heat maps of original pixels in the receptive field participating in the convolution operation are quantified and generated. According to the generated heat map, the feature extraction optimization method of convolution combinations with different dilation rates is proposed, and an efficient convolution feature extraction network containing three kinds of dilated convolutions is designed. Combined with the O-ring surface defect features, a multiscale defect detection network is designed. Before the head of multiscale classification and position regression, feature fusion tree modules are added to ensure the reuse and compression of the responsive features of different receptive fields on the same scale feature maps. Experimental results show that on the O-rings-3000 testing dataset, the mean condition accuracy of the proposed algorithm reaches 95.10% for 5 types of surface defects of aerospace O-rings. Compared with RefineDet, the mean condition accuracy of the proposed algorithm is only reduced by 1.79%, while the parameters and FLOPs are reduced by 35.29% and 64.90%, respectively. Moreover, the proposed algorithm has good adaptability to image blur and light changes caused by the cutting of imaging hardware, thus saving the cost.

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