SQUASH logo TPO logo

Standard compliant QUAlity control System for High-level ceramic material manufacturing (SQUASH)
INCO-COPERNICUS, grant no. ERBIC 15CT 96 0742. 1997-98.
Co-ordinated by the University of Cagliari, Italy,
in cooperation with AET - Advanced Engineering Technology S.r.l., Italy

The Association for Image Processing, Poland, was one of the contractors.
The main page of the Project

Abstract of the results obtained and owned by the Association for Image Processing, PL

Detection and Classification of Surface Irregularities
in Black Ceramics

L. Chmielewski, M. Skłodowski, W. Cudny, M. Nieniewski
Association for Image Processing
and Institute of Fundamental Technological Research, PAS
A. Jóźwik
Institute of Biocybernetics and Biomedical Engineering, PAS

Presentation of the Project
(PowerPoint, 2.5 MB)
given at the
International Workshop on Structural Image Analysis in Investigation of Concrete
October 21-23, 2002, Warsaw, Poland
organized by the
Institute of Fundamental Technological Research, PAS - Center of Excellence for Advanced Materials and Structures

1 Overview

Manufacturers and buyers of black ceramics distinguish numerous classes of surface irregularities (defects) of black ceramics. However, only some irregularities of selected surfaces have been chosen for the present research, due to their high importance to the quality of the final products.
    The objects considered are mainly hard and soft ferrite materials: ferrite cores and magnets. Other materials of similar appearance can be inspected in the same or similar way.
    The monochromatic images of the irregularities of interest are acquired by the system consisting of a highly specialised lighting system [EPS97], a standard digital camera, and a computer.
    A hierarchical method for classification of a core with respect to surface irregularities has been applied. It consists of two main phases: The whole method is complemented by postproccessing the results of the classification phase: the context information is taken into account by a local weighted voting process.
    Finally, the irregularities are measured, and the results are compared with quality standards to get the final classification of the product as belonging to a respective quality class. Quantitative nature of the measurements make them useful in analysis of the production process.

2 Types of considered irregularities

The surface irregularities considered are: chips, pull-outs and cracks (Fig. 1).

Fig. 1. Various types of irregularities: a: chips; b: pull-outs; c: pull-out and crack; d: various, might be difficult to discern.

3 Training and classification

The classifier applied to feature-based classification of the pixels marked as possibly irregular by the detection phase is a highly advanced version of the K-NN method, having the  following properties: 1o fuzzy, 2o parallel (1 classifier for each pair of classes), 3o with full selection of features, 4o hierarchical (a simple 1-NN version is used where classes do not overlap in the feature space), 5o with reduced reference patterns set. This combination leads to high speed and accuracy of classification.
    The used 64 features were the textural features [Law80] and local section of brightness function [CSCMJ97].
    From the point of view of the user it is important how to prepare the data for training the classifier. The training process has been organised in such a way that its main part consists in showing examples of image pixels which belong to all classes defined for the specific problem. This is done by manually marking such pixels in colours (Fig.2). A large number of training pixels (training patterns) coming from each class should be given to obtain good final classification results. In the examples presented in Fig. 2, there were 4297 training pixels prepared. With this large set, the global classification error rate estimated by the classifier on the training set was was 3.3% (with the leave-one-out method [Bez86, Das95, CSCMJ97]). Keeping this measure of error small is a necessary, but not a sufficient condition of receiving a classifier with acceptable generalisation power (ability to correctly classify the patterns which it has never seen). Good final results for a large set of example images should be received first - see Fig. 2.
    It is the experience gained in this research that for good classification, each class should be divided into subclasses. Let us take an example of a chip. The following six derived classes have been introduced: dark chip, bright chip, edge between a chip (dark and bright separately) and good object, edge between a chip (dark and bright) and background. After classification, all these classes are merged into one: chip.
    The fully automated process of training the classifier consists in forming the reduced sets of reference patterns for all class pairs, selecting the most significant features for each pair of classes and choosing K (the number of neighbours in the feature space) for each pair. The size (power) of the set of reference patterns, which is the set of representant patterns for each class, is usually less than 10% of the size of the set of training patterns.

4 Examples of results

The results of classification of the images of Figs. 2 a1 and a2 can be seen in Figs. d1 and d2 (Good pixels were not marked). Only those pixels which were detected as irregular by the detection phase (Figs. 2 c1 and b2) were classified. After taking into account the context information, the separated single pixels disappear (Fig. 2 c2 and d2), and the image is ready for measuring the blobs corresponding to various classes of irregularities.

Fig. 2. Examples of detection and classification of irregularities.
a1: input image 1 (some pixels of this image were taken for training - see b1); b1: training patterns taken from image a1 (blue: chip, green: pull-out, red: crack, yellow: good object); c1: detected irregular regions (brown); d1: results of classification after postprocessing.
a2: input image 2 (no pixels of this image were taken for training); b2: detected irregular regions; c2: raw results of classification. d2: results of classification after postprocessing.

The object shown in Fig. 2 a2 has the diameter of 10 mm. The displayed part of the image has 200*200 pixels (0.0568 mm/pix). The following results have been obtained for that object:

Total area of irregularities: 6.18 [mm2]
# CLASS blobs type max blob total quality
3 CHIP 12 AREA 0.11/0.50 [mm2] 0.57/1.00 [mm2] GOOD
7 PULL-OUT 24 AREA 0.89/0.50 [mm2] 1.76/1.00 [mm2] BAD!
10 CRACK 19 LENGTH 3.46/1.50 [mm] 11.76/3.00 [mm] BAD!
!!! this object is BAD !!!

The classification took about 12.4 s (Pentium, 200 Mhz; note that for an object with less irregularities the classification would be proportionally quicker - classification of one pixel - 0.0032 mm2 - takes approx. 6.5 ms). The detection carried out by software took 10 s (the same process would take less than 0.05 s if a typical hardware morphological processor were used).

5 Conclusions

TPO logo A hierarchical computerised optical inspection system for detection and classification of defects (called irregularities) on flat surfaces of products of black ceramics has been designed and implemented. Special attention was paid to overcoming the difficulties related to dark colour of the tested objects and to attain invariance of the phases of inspection to such noncontrollable factors like scale of the defects and location of the object.
    The system has been found to work properly in a series of tests. Quantitative nature of the results make it possible to carry out various kinds of analyses of the production process.
    The methodology has been developed up to a level at which an industrial implementation is possible. For this purpose, no other highly specialised hardware seem to be necessary than a mathematical morphology processor.
    The proposed methods are applicable to detection of defects on flat surfaces of numerous similar (ferrite cores, magnets, sintered carbides) and dissimilar materials.


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Updated: Oct 23, 2002.   Last maintained: Oct 23, 2002.     Contact: Leszek Chmielewski, e-mail: lchmiel@ippt.gov.pl.