Auto-detection of Anisakid larvae in Cod Fillets by UV fluorescent imaging with OS-ELM

Wenqiang Cai, Limin Cao, Hong Lin, Jianxin Sui, Rui Nian, Jidong Hu, Amaury Lendasse

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


In this paper, one auto-detection scheme of Anisakid larvae in cod fillets is developed on the basis of online sequential extreme learning machine (OS-ELM) in a single hidden layer feedforward neural networks (SLFN). One UV fluorescent imaging system is first set up to collect and extract the typical image patches with and without Anisakid larvae inside the fish muscles, the UV fluorescent image patches are then fed into SLFN sequentially to learn how to nondestructively identify the parasites in real-time, particularly for a growing size of the training set with new observations arrived again and again. It has been shown in the simulation experiments that the developed nondestructive approach could get online auto-detection performance in both good accuracy and efficiency during the test, even for those Anisakid larvae deeply embedded in the cod fillets.

Original languageEnglish
Title of host publicationTENCON 2015 - 2015 IEEE Region 10 Conference
Number of pages5
ISBN (Electronic)978-1-4799-8641-5
ISBN (Print)978-1-4799-8639-2
Publication statusPublished - 5 Jan 2016
MoE publication typeA4 Article in a conference publication
EventIEEE Region 10 Conference - Macau, Macao
Duration: 1 Nov 20154 Nov 2015
Conference number: 35

Publication series

ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


ConferenceIEEE Region 10 Conference
Abbreviated titleTENCON
Internet address


  • Anisakid Larvae
  • Auto-detection
  • Online Sequential Extreme Learning Machine
  • Single Hidden Layer Feedforward Neural Networks
  • UV Fluorescent Imaging

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