Surface inspection is important in many industrial production processes. There are applications from timber grading to machine part inspection. The problem can be divided into two independent parts, surface classification and fault detection. Both fields have been subjects of active research since the late 1970's [2, 4, 10]. The methods have usually been based on some simple features, as gray values or colours, and on a thresholding logic. Recently, neural network methods have become popular. The reason has been the possibility to use more realistic decision surfaces by neural networks. However, some of the methods have been based on a simple one-layer neural network, the Self-Organizing Map (SOM) [9]. Visa [14], for instance, developed a surface classification method based on a SOM, a Learning Vector Quantization [9], and a long feature vector consisting of texture measures. The same idea was extended to a textured image segmentation by Visa [15]. Later on Yläkoski and Visa developed the method further for timber classification [17]. The extension of the method consisted of a grammar that described the quality of a surface by the means of defects. SOM has not been a popular method in fault detection, but there are some attempts. Kasslin et al. used a SOM to monitor the state of a electrical device [8]. The sequence of the states of the device, a trace, was visualized on the SOM. The deviations from the ordinary trace were interpreted as faults. Alander et al. [1] and Tryba and Goser [12] used a SOM to monitor a coffee maker to control a process in chemistry. Vapola et al. used a similar approach but two layers of SOM to monitor an anaesthesia system [13].
In this paper the mentioned ideas will be extended to a special case of image segmentation, fault detection. In section two it will be described how to separate the faults from the background. Some results are demonstrated and compared with a thresholding method in section three. The limitations of the suggested method are discussed in section four.