Non-destructive evaluation is widely used in the manufacturing industry for the detection and characterisation of defects. Typical techniques include visual, magnetic particle, fluorescent dye penetrant, ultrasonic, and eddy current inspection. This paper presents a multi-agent approach to combining image data such as these for quality control. The use of distributed agents allows the speed benefits of parallel processing to be realised, facilitating increased levels of detection through the use of high resolution images. The integration of multi-sensor devices and the fusion of their multi-modal outputs has the potential to provide an increased level of certainty in defect detection and identification. It may also allow the detection and identification of defects that cannot be detected by an individual sensor. This would reduce uncertainty and provide a more complete picture of aesthetic and structural integrity than is possible from a single data source.
A blackboard architecture, DARBS (Distributed Algorithmic and Rule-based Blackboard System), has been used to manage the processing and interpretation of image data. Rules and image processing routines are allocated to intelligent agents that communicate with each other via the blackboard, where the current understanding of the problem evolves. Specialist agents register image segments into a common coordinate system. An intensity-based algorithm eliminates the landmark extraction that would be required by feature-based registration techniques. Once registered, pixel-level data fusion is utilised so that both complementary and redundant data can be exploited. The modular nature of the blackboard architecture allows additional sensor data to be processed by the addition or removal of specialised agents.