Colour bake inspection system using hybrid artificial neural networks

Jeffrey Yeh, Leonard G C Hamey, Tas Westcott, Samuel Sung

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

26 Citations (Scopus)

Abstract

The bake level of biscuits is of significant value to biscuit manufacturers as it determines the taste, texture and appearance of the products. Previous research explored and revealed the feasibility of biscuit bake inspection using feed forward neural networks (FFNN) with a back propagation learning algorithm and monochrome images. A second study revealed the existence of a curve in colour space, called a baking curve, along which the bake colour changes during the baking process. Combining these results, we proposed an automated bake inspection system with artificial neural networks that utilizes colour instead of monochrome images. In this paper, we present the implementation of the inspection system with a hybrid neural network of self-organizing maps and FFNNs. The system was tested and its grading performance on biscuit bake levels was evaluated and compared to that of a trained human inspector. We found that the proposed colour system with a hybrid neural network performed significantly better than the human inspector.

Original languageEnglish
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
Place of PublicationPiscataway, N.J.
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages37-42
Number of pages6
Volume6
ISBN (Print)0780327683
DOIs
Publication statusPublished - Dec 1995
EventProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6) - Perth, Aust
Duration: 27 Nov 19951 Dec 1995

Other

OtherProceedings of the 1995 IEEE International Conference on Neural Networks. Part 1 (of 6)
CityPerth, Aust
Period27/11/951/12/95

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