A Machine Vision Approach for Recognizing Coastal Fish

Authors

  • Afiq Raihan Daffodil International University, Bangladesh
  • Israt Sharmin Daffodil International University, Bangladesh
  • B M Marjan Khan Daffodil International University, Bangladesh
  • Md. Ismail Jabiullah Daffodil International University, Bangladesh
  • Md. Tarek Habib Daffodil International University, Bangladesh

DOI:

https://doi.org/10.4114/intartif.vol25iss70pp13-32

Keywords:

Fish species recognition, Machine vision, Feature extraction, Principal component analysis, k-nearest neighbor, Performance metric

Abstract

Coastal fish is one of the prominent marine resources, which takes a necessary role in the economic growth of a country. Because of environmental issues along with other reasons, not only most of the marine resources are diminishing but also many coastal fishes are getting extinct gradually. As a result, the young peoples have insufficient knowledge of coastal fish. This issue can be solved with the use of vision-based technologies. To deal with this situation, a coastal fish recognition system based on machine vision is conceived, which can be approached by the images of coastal fish that are captured with a portable device and identify the fish to recognize fish. Numerous experimental analyses are executed to exhibit the benefit of this proposed expert system. In the beginning, conversion of a color image into a gray-scale image occurs and the gray-scale histogram is developed. Using the histogram-based method, image segmentation is conducted. After that, a set of thirteen features comprising of four classes is extracted to be fed to a classifier. For reducing the number of features, PCA is applied. To recognize coastal fish, three cutting-edge classifiers are performed, where k-NN provides a potential accuracy of up to 98.7%.

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Published

2022-09-25

How to Cite

Afiq Raihan, Israt Sharmin, B M Marjan Khan, Md. Ismail Jabiullah, & Md. Tarek Habib. (2022). A Machine Vision Approach for Recognizing Coastal Fish . Inteligencia Artificial, 25(70), 13–32. https://doi.org/10.4114/intartif.vol25iss70pp13-32