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Performance issues of an Automated Visual Inspection System (AVIS)

Efforts have been made to utilize AI constructs to identify flaws in the Space Shuttle Main Engine (SSME) faceplate regions. In order to expand the applicability of these algorithms to a larger problem domain, the automatic visual inspection system(AVIS) has been modified to enable a user with little or no image processing background to define a system capable of identifying flaws on a given set of imagery. This system requires the user to simply identify flawed regions and the selection of processing and feature descriptors is performed automatically. This paper explicates the motivations, definitions, and performance issues associated with the AVIS paradigm. INTRODUCTION Beginning in 1991, an effort has been made to utilize AI techniques for the identification of flaw characteristics in SSME engine images[1][2][3][4]. The first phase of this research emphasized the development of algorithms to segment and identify morphological anomalies in the engine faceplate region(Fig. 1). The second phase involved analysis of discoloration flaws and development of a multiresolution analysis of the images. The problems addressed with this research have been that of model representation, AI black board schemes, classification methods, and image processing. In order for flawed regions to be correctly identified a set of algorithms for each flaw type had to be constructed. These algorithms consisted of model based analysis in conjunction with a set of image processing tasks(filtering, transformations, histogram modification, segmentation, ...etc.). The product of these operations is a set of features which must be classified ( i.e. region of interest, flawed region, lighting anomaly). The orchestration of the appropriate set of tasks based on these feature classifications is performed using a blackboard AI structure. Fig. 1– Diagram of SSME faceplate region