fields
Corresponding
area Total items number Class 1 items number Class -1 items number
A 1 1178 28 cm² 3865 275 3590
B 2 605 14 cm² 1910 184 1726
Table 1. Description of the two databases used for validation experiments
class 1 classification and class -1 classification perfor-
mances. It shows first that equivalent performances can
be obtained using only 5-dimensional data instead of unprocessed defects representations (13-dimensional). As a consequence neural architecture complexity and therefore processing time can be saved using CDA dimensionality reduction, while keeping performance level. Moreover, obtained scores are satisfactory: about
70% of “dust” defects are well-recognized (this can be
enough for aimed application) as well as about 97% of other defects (the few 3% errors can however pose
problems because every “permanent” defect has to be reported). Furthermore, we think that this significant
performances difference between class 1 and class -1 recognition is due to the fact that class 1 is underrep- resented in learning database.
Figure 4. Classification performances for different CDA issued data dimensionality. Classification performances using raw data (13-dimensional) are also depicted as dotted lines.
50 55 60 65 70 75 80 85 90 95 100 2 3 4 5 6 7 8 9 10 11 12 13 Data Dimensionality % o f c la s s if ie r c o rr e c t a n s w e rs
Class -1 c lassification pe rform ance Class 1 classification per form ance Global classification per form ance
FUTURE TRENDS
Next phase of this work will deal with classification
tasks involving more classes. We want also use much more Fourier-Mellin invariants, because we think
that it would improve classification performance by
supplying additional information. In this case, CDA based dimensionality reduction technique would be a
foremost step to keep reasonable classification system’s
complexity and processing time.
CONCLUSION
A reliable diagnosis of aesthetic flaws in high-quality
optical devices is a crucial task to ensure products’
nominal specification and to enhance the production
quality by studying the impact of the process on such defects. To ensure a reliable diagnosis, an automatic system is needed to detect defects and secondly dis-
ANN-Based Defects’ Diagnosis of Industrial Optical Devices
criminate the “false” defects (correctable defects) from “abiding” (permanent) ones. In this paper is described
a complete framework, which allows detecting all de- fects present in a raw Nomarski image and extracting
pertinent features for classification of these defects. Obtained proper performances for “dust” versus “other” defects classification task with MLP neural network has
demonstrated the pertinence of proposed approach. In addition, data dimensionality reduction permits to use
low complexity classifier (while keeping performance
level) and therefore to save processing time.
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KEy TERMS
Artificial Neural Networks: A network of many
simple processors (“units” or “neurons”) that imitates
a biological neural network. The units are connected by unidirectional communication channels, which carry numeric data. Neural networks can be trained
ANN-Based Defects’ Diagnosis of Industrial Optical Devices