Classification of Coral Reef Submarine Images and Videos Using a Novel Z with Tilted Z Local Binary Pattern (Z⊕TZLBP)

Mary, Nirmala; Dejey,

doi:10.1007/s11277-017-4981-x

Cited by 24 publications

(4 citation statements)

References 40 publications

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“…Beijbom [13,27]. It proposed the Z with Tilted Z Local Binary Pattern (Z+TZLBP) [28] and the texture-based Octa-angled pattern for triangular sub-regions (OPT) [29] for the classification of coral-reef images and videos. Mohammad Hossein Shakoor and Reza Boostani derived a coral-reef image classification system using the local binary pattern [30].…”

Section: Related Workmentioning

confidence: 99%

MAFFN_YOLOv5: Multi-Scale Attention Feature Fusion Network on the YOLOv5 Model for the Health Detection of Coral-Reefs Using a Built-In Benchmark Dataset

Sivamani

Nedumaran

2023

Analytics

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Coral-reefs are a significant species in marine life, which are affected by multiple diseases due to the stress and variation in heat under the impact of the ocean. The autonomous monitoring and detection of coral health are crucial for researchers to protect it at an early stage. The detection of coral diseases is a difficult task due to the inadequate coral-reef datasets. Therefore, we have developed a coral-reef benchmark dataset and proposed a Multi-scale Attention Feature Fusion Network (MAFFN) as a neck part of the YOLOv5′s network, called “MAFFN_YOLOv5”. The MAFFN_YOLOv5 model outperforms the state-of-the-art object detectors, such as YOLOv5, YOLOX, and YOLOR, by improving the detection accuracy to 8.64%, 3.78%, and 18.05%, respectively, based on the mean average precision (mAP@.5), and 7.8%, 3.72%, and 17.87%, respectively, based on the mAP@.5:.95. Consequently, we have tested a hardware-based deep neural network for the detection of coral-reef health.

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Section: Related Workmentioning

confidence: 99%

MAFFN_YOLOv5: Multi-Scale Attention Feature Fusion Network on the YOLOv5 Model for the Health Detection of Coral-Reefs Using a Built-In Benchmark Dataset

Sivamani

Nedumaran

2023

Analytics

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“…The authors in [25,26,27] proposed three different modifications of the local 190 binary pattern feature descriptor, and they used the extracted features to train machine learning models in order to classify several datasets.…”

Section: Previous Workmentioning

confidence: 99%

“…In 2015, the author in [7] used CNNs for the first time to resolve this task. In [10], the authors proposed the use of more powerful CNNs to classify EILAT and RSMAS [21], and they found that ResNet was the best CNN architecture in both datasets, improving the state-of-the-art results obtained by [5,25,26] and [27], which were all the works that used EILAT and RSMAS.…”

Section: Previous Workmentioning

confidence: 99%

Coral species identification with texture or structure images using a two-level classifier based on Convolutional Neural Networks

Gómez-Ríos

Tabik

Luengo

et al. 2019

Knowledge-Based Systems

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“…To classify coral from non coral Resfeats,convolutional neural network are utilized. [24] proposed Z with Tilted Z Local Binary Pattern (ZTZLBP) for coral reef imageextraction.KNN is used for image classification.…”

Section: Related Workmentioning

confidence: 99%

Coral Reef Image Classifications with Hybrid Methods

2019

IJITEE

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There are several organisms on oceans. Among the organisms coral reefs are the one with 800 species. Classifying coral is a difficult task. Scientist classify the coral organism and put in to groups based on their characteristics. There are several machine learning algorithms are implemented to analyzer and classify the coral species. The main aim of this work is to effectively use handcrafted features with deep features for classifying the coral classes. Here the state of art feature descriptors such as Local Binary Pattern, Local Arc Pattern and Improved Webbers Binary Code are proposed to extract the features of coral. The results which obtained can be further improved by combining these local descriptors with convolution neural network .The feature extracted by above methods are classified using KNN and Random Forest. Experiments with these methods are conducted using EILAT dataset. The Experimental results obtained by these methods demonstrate the effectiveness and robustness of our proposed method.

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Classification of Coral Reef Submarine Images and Videos Using a Novel Z with Tilted Z Local Binary Pattern (Z⊕TZLBP)

Cited by 24 publications

References 40 publications

MAFFN_YOLOv5: Multi-Scale Attention Feature Fusion Network on the YOLOv5 Model for the Health Detection of Coral-Reefs Using a Built-In Benchmark Dataset

MAFFN_YOLOv5: Multi-Scale Attention Feature Fusion Network on the YOLOv5 Model for the Health Detection of Coral-Reefs Using a Built-In Benchmark Dataset

Coral species identification with texture or structure images using a two-level classifier based on Convolutional Neural Networks

Coral Reef Image Classifications with Hybrid Methods

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