Machine learning framework for precise localization of bleached corals using bag-of-hybrid visual feature
Fawad1, Iftikhar Ahmad1, Arif Ullah1
1Department of Computer Engineering, Chosun University, Gwangju, 61452, Republic of Korea.
Scientific Reports
|November 9, 2023
Summary
Researchers developed a robust method to locate bleached corals using hybrid visual features and deep learning, achieving 96.20% accuracy. This technique improves marine pharmacognosy research by accurately identifying coral bleaching in the Great Barrier Reef.
Area of Science:
- Marine Biology
- Computer Vision
- Artificial Intelligence
Background:
- Coral reefs are vital underwater ecosystems threatened by temperature-sensitive bleaching.
- Bleached corals impact marine pharmacognosy, necessitating accurate localization for restoration efforts.
- Existing visual classification methods struggle with varying illumination, orientation, scale, and view angles.
Purpose of the Study:
- To develop a highly noise-robust and invariant localization method for bleached corals.
- To improve the accuracy and reliability of bleached coral detection in the Great Barrier Reef.
- To address the limitations of current visual classification techniques.
Main Methods:
- Implemented a localization using bag-of-hybrid visual features (RL-BoHVF) approach.
- Employed AlexNet deep neural network (DNN) combined with ColorTexture handcrafted features.
- Utilized a bag-of-feature method to reduce dimensionality and enhance robustness.
Main Results:
- Achieved a classification accuracy of 96.20% on a balanced dataset from the Great Barrier Reef.
- Demonstrated superior localization performance compared to existing standalone and hybrid models.
- Evaluated the model on 342 images, confirming its effectiveness across train and test segments.
Conclusions:
- The proposed RL-BoHVF method offers a significant advancement in bleached coral localization.
- The hybrid approach combining DNN and handcrafted features provides high accuracy and robustness.
- This technique supports critical research in marine conservation and pharmacognosy.
