Combining MobileNetV1 and Depthwise Separable convolution bottleneck with Expansion for classifying the freshness of fish eyes

Eko Prasetyo*, Rani Purbaningtyas, Raden Dimas Adityo, Nanik Suciati, Chastine Fatichah

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)

Abstract

Image classification using Convolutional Neural Network (CNN) achieves optimal performance with a particular strategy. MobileNet reduces the parameter number for learning features by switching from the standard convolution paradigm to the depthwise separable convolution (DSC) paradigm. However, there are not enough features to learn for identifying the freshness of fish eyes. Furthermore, minor variances in features should not require complicated CNN architecture. In this paper, our first contribution proposed DSC Bottleneck with Expansion for learning features of the freshness of fish eyes with a Bottleneck Multiplier. The second contribution proposed Residual Transition to bridge current feature maps and skip connection feature maps to the next convolution block. The third contribution proposed MobileNetV1 Bottleneck with Expansion (MB-BE) for classifying the freshness of fish eyes. The result obtained from the Freshness of the Fish Eyes dataset shows that MB-BE outperformed other models such as original MobileNet, VGG16, Densenet, Nasnet Mobile with 63.21% accuracy.

Original languageEnglish
Pages (from-to)485-496
Number of pages12
JournalInformation Processing in Agriculture
Volume9
Issue number4
DOIs
Publication statusPublished - Dec 2022

Keywords

  • Bottleneck
  • Classification
  • Depthwise separable convolution
  • Fish eye
  • Freshness
  • Residual transition

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