Data for: A league-winner algorithm for defect classication in an industrial web inspection system

Published: 26 February 2021| Version 1 | DOI: 10.17632/sb9cn97sxb.1
Angel Gaspar Gonzalez-Rodriguez


Data sheets with the set of defects used for training and testing the classiffiers. Columns B to BC (if read with Excel) are the value of 54 descriptors. Following two columns are the defect labelling given by an expert during the supervised training (both as a char or as an index). char E -> 6 fish-eye char I -> 0 gel char O -> 1 hole char N -> 2 Black Speck char B -> 3 Bug/Insect char A -> 4 Wrinkle char G -> 5 Rubber char D -> 7 Oil Drop char L -> 8 Light dirt char F -> 9 Fail Transmission First column is the name of the defect. Its first letter should be coherent with last two columns for defects with the following exceptions: - defects named as JXXXX.bmp are defects that can be considered as gel or rubber - defects named as KXXXX.bmp are defects that can be considered as gel or black speck - defects named as MXXXX.bmp are defects that can be considered as rubber or black speck



Artificial Neural Networks, Pattern Recognition Classification Process