Identification of Underwater Species Using Condition-Based Ensemble Supervised Learning Classification

Authors

  • Vinston Raja R. Assistant Professor, Department of Information Technology, Panimalar Engineering College.
  • Adithya V. Student, Department of Information Technology, Panimalar Institute of Technology
  • Flintoff A. Hollioake Student, Department of Information Technology, Panimalar Institute of Technology
  • Krishna Kumar N. Student, Department of Information Technology, Panimalar Institute of Technology
  • Kirran P. L. Student, Department of Information Technology, Panimalar Institute of Technology
  • G. Dhanalakshmi Student, Department of Information Technology, Panimalar Institute of Technology

Keywords:

R-CNN, underwater images, fish species, deep learning, acquiring fish body images, segment, morphological

Abstract

The aim of this research paper is to use deep learning and R-CNN features with open cv tools to detect and recognize different fish species from underwater images. The RCNN method uses a selective search algorithm to extract the top 2000 region proposals from millions of Regions of Interest (RoI) proposals in an image, which is then inputted to a CNN model for further analysis. The deep learning approach used for fish recognition achieved 85% accuracy. The focus of this study is on fish recognition in a natural lake to aid in preserving the original environment. To achieve this, the paper proposes a scheme for segmenting fish images and measuring their morphological features using R-CNN. The process starts with acquiring fish body images using a homemade image acquisition device, preprocessing and labeling the images, and then training the R-CNN model with the labeled images. The fish images are segmented using the trained model, enabling the extraction of morphological characteristics that serve as indicators of the fish.

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References

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Published

16.07.2023

How to Cite

Raja R., V. ., V., A. ., Hollioake, F. A. ., N., K. K. ., P. L., K. ., & Dhanalakshmi, G. . (2023). Identification of Underwater Species Using Condition-Based Ensemble Supervised Learning Classification. International Journal of Intelligent Systems and Applications in Engineering, 11(3), 01–12. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3137

Issue

Vol. 11 No. 3 (2023)

Section

Research Article

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