CREATION OF IMAGE MODELS FOR INSPECTing DEFECTS IN COMMERCIAL DRIED FISH FLOSS
Fish floss is a chopped finely or mashed fish meat boiled in seasonings, then stir fry until the fish meat is arid and
pulverous. In the making of commercial fish flosses, defect inspection is conducted by expertise inspectors using
their feeling of contact and sight which could cause misjudgments. When consumers eat fish floss with defects, it may
cause harm to the health of consumers. Therefore, this study proposes an automated defect detection method and
develop an optical inspection system for commercial dried fish floss. The proposed method applies the curvelet transform with low-pass energy filtering to remove the random patterns of background and delete the angle direction of
background texture. The approximated and partial detailed components regarding defects and uniform background
are preserved in the low and medium frequency bands. In the reconstructed image, the background random texture
is attenuated and the defect areas are enhanced. Finally, the restored image can be easily segmented by an estimated threshold value into two categories namely dark defects, and white background. The experimental results show
that the proposed method well balances the trade-off between the recall rate (82.11%) and precision rate (87.62%),
and reaches an F-score of 84.78%, outperforming the traditional defect detection techniques in inspection of dried
This research was fractionally sponsored by the Ministry
of Science and Technology, Taiwan, for the financial assistance through the Grant MOST 107-2221-E-324-016.
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