Automatic labeling of fish species using deep learning across different classification strategies

  1. Jareño, Javier 2
  2. Bárcena-González, Guillermo 2
  3. Castro-Gutiérrez, Jairo 1
  4. Cabrera-Castro, Remedios 1
  5. Galindo, Pedro L. 2
  1. 1 Biology Department, University of Cádiz, Cádiz, Spain
  2. 2 Computer Science Department, University of Cádiz, Cádiz, Spain
Zeitschrift:
Frontiers in Computer Science

ISSN: 2624-9898

Datum der Publikation: 2024

Ausgabe: 6

Seiten: 1-12

Art: Artikel

DOI: 10.3389/FCOMP.2024.1326452 GOOGLE SCHOLAR lock_openOpen Access editor

Andere Publikationen in: Frontiers in Computer Science

Zusammenfassung

Convolutional neural networks (CNNs) have revolutionized image recognition.Their ability to identify complex patterns, combined with learning transfertechniques, has proven eective in multiple fields, such as image classification. Inthis article we propose to apply a two-step methodology for image classificationtasks. First, apply transfer learning with the desired dataset, and subsequently, ina second stage, replace the classification layers by other alternative classificationmodels. The whole methodology has been tested on a dataset collected atConil de la Frontera fish market, in Southwest Spain, including 19 dierentfish species to be classified for fish auction market. The study was conductedin five steps: (i) collecting and preprocessing images included in the dataset,(ii) using transfer learning from 4 well-known CNNs (ResNet152V2, VGG16,EcientNetV2L and Xception) for image classification to get initial models, (iii)apply fine-tuning to obtain final CNN models, (iv) substitute classification layerwith 21 dierent classifiers obtaining multiple F1-scores for dierent trainingtest splits of the dataset for each model, and (v) apply post-hoc statisticalanalysis to compare their performances in terms of accuracy. Results indicatethat combining the feature extraction capabilities of CNNs with other supervisedclassification algorithms, such as Support Vector Machines or Linear DiscriminantAnalysis is a simple and eective way to increase model perfomance.

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