Toward an automated rivet inspection system for aging aircraft

This thesis describes work on an automated rivet inspection system for aging aircraft using magneto-optic imaging (MOI). MOI is a non-destructive evaluation technique that is being used increasingly in aircraft inspection. Even though MOI offers high efficiency in non-destructive inspection, the large area of material that needs periodic inspection has created a need for more efficient data interpretation methods: an automated inspection system. The proposed inspection algorithm focuses on rivets that are one of the common places where cracks originate.; Motion-Based Filtering (MBF) is developed as an effective filtering method for MOI images. MBF extracts only "moving objects" in a sequence of images and suppresses stationary background by using a multiple frame subtraction method. The filtered images are processed with rivet detection algorithms to properly locate rivets. Two rivet detection algorithms are developed based on Hough transformation and morphological operation. The detected rivets are classified by classification algorithms implemented by Hough transformation or Bayes decision rule.; The off-line test of the prototype automated rivet inspection system on 245 MOI rivet images showed up to 98% accuracy, but more data is needed for testing. Work is shown in speeding up the algorithms for possible real-time use. A proof-of-concept inspection system showed the capability of processing 3 to 5 images per second.