Surface Inspection Using 3-tier Neural Classification
نویسندگان
چکیده
In the field of surface inspection, objects in surfaces are detected, classified and labelled. This is important in today’s industry since detecting defects in the raw material is crucial to assure the quality of the merchandise. The thesis discusses the development phases in the automated construction of an image understanding system. It introduces novel techniques for the robust detection of peaks through feed-forward neural networks, and applies them to a 3-tier size and rotation invariant object isolation and labelling scheme. An exemplary instantiation of the generic architecture was tested on industrial surface inspection cases. The results indicate that the architecture is successfully applied to different problems while only requiring minor adaptations. The high performance of the neural classifier when used in these industrial situations showed the approach to be very suitable.
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