Bag of Features (BoF) Based Deep Learning Framework for Bleached Corals Detection

Author:

Jamil SonainORCID,Rahman MuhibUrORCID,Haider AmirORCID

Abstract

Coral reefs are the sub-aqueous calcium carbonate structures collected by the invertebrates known as corals. The charm and beauty of coral reefs attract tourists, and they play a vital role in preserving biodiversity, ceasing coastal erosion, and promoting business trade. However, they are declining because of over-exploitation, damaging fishery, marine pollution, and global climate changes. Also, coral reefs help treat human immune-deficiency virus (HIV), heart disease, and coastal erosion. The corals of Australia’s great barrier reef have started bleaching due to the ocean acidification, and global warming, which is an alarming threat to the earth’s ecosystem. Many techniques have been developed to address such issues. However, each method has a limitation due to the low resolution of images, diverse weather conditions, etc. In this paper, we propose a bag of features (BoF) based approach that can detect and localize the bleached corals before the safety measures are applied. The dataset contains images of bleached and unbleached corals, and various kernels are used to support the vector machine so that extracted features can be classified. The accuracy of handcrafted descriptors and deep convolutional neural networks is analyzed and provided in detail with comparison to the current method. Various handcrafted descriptors like local binary pattern, a histogram of an oriented gradient, locally encoded transform feature histogram, gray level co-occurrence matrix, and completed joint scale local binary pattern are used for feature extraction. Specific deep convolutional neural networks such as AlexNet, GoogLeNet, VGG-19, ResNet-50, Inception v3, and CoralNet are being used for feature extraction. From experimental analysis and results, the proposed technique outperforms in comparison to the current state-of-the-art methods. The proposed technique achieves 99.08% accuracy with a classification error of 0.92%. A novel bleached coral positioning algorithm is also proposed to locate bleached corals in the coral reef images.

Publisher

MDPI AG

Subject

Artificial Intelligence,Computer Science Applications,Information Systems,Management Information Systems

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1. A novel approach to coral species classification using deep learning and unsupervised feature extraction;Journal of Spatial Science;2024-08-14

2. Mapping emergent coral reefs: a comparison of pixel‐ and object‐based methods;Remote Sensing in Ecology and Conservation;2024-05-29

3. Beyond pH Levels: A Comprehensive Survey on Ocean Acidification;2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE);2024-02-22

4. Automatic Coral Detection with YOLO: A Deep Learning Approach for Efficient and Accurate Coral Reef Monitoring;Communications in Computer and Information Science;2024

5. Automatic Coral Morphotypes Detection with YOLO: A Deep Learning Approach for Efficient and Accurate Coral Reef Monitoring;IFIP Advances in Information and Communication Technology;2024

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