Department of Software and Information Systems, Faculty of Information, Communication and Digital Technologies, University of Mauritius, Reduit, Mauritius.
Department of Agricultural and Food Science, Faculty of Agriculture, University of Mauritius, Reduit, Mauritius.
PLoS One. 2023 Apr 5;18(4):e0283121. doi: 10.1371/journal.pone.0283121. eCollection 2023.
Coral reefs play important roles in the marine ecosystem, from providing shelter to aquatic lives to being a source of income to others. However, they are in danger from outbreaks of species like the Crown of Thorns Starfish (COTS) and the widespread coral bleaching from rising sea temperatures. The identification of COTS for detecting outbreaks is a challenging task and is often done through snorkelling and diving activities with limited range, where strong currents result in poor image capture, damage of capturing equipment, and are of high risks. This paper proposes a novel approach for the automatic detection of COTS based Convolutional Neural Network (CNN) with an enhanced attention module. Different pre-trained CNN models, namely, VGG19 and MobileNetV2 have been applied to our dataset with the aim of detecting and classifying COTS using transfer learning. The architecture of the pre-trained models was optimised using ADAM optimisers and an accuracy of 87.1% was achieved for VGG19 and 80.2% for the MobileNetV2. The attention model was developed and added to the CNN to determine which features in the starfish were influencing the classification. The enhanced model attained an accuracy of 92.6% while explaining the causal features in COTS. The mean average precision of the enhanced VGG-19 with the addition of the attention model was 95% showing an increase of 2% compared to only the enhanced VGG-19 model.
珊瑚礁在海洋生态系统中扮演着重要的角色，为水生生物提供庇护所，也是其他生物的收入来源。然而，它们正面临着棘冠海星（COTS）等物种爆发和海水温度上升导致的珊瑚白化的威胁。识别 COTS 以检测爆发是一项具有挑战性的任务，通常通过有限范围的浮潜和潜水活动来完成，在这些活动中，强烈的水流会导致图像捕捉质量差、捕捉设备损坏，并且风险很高。本文提出了一种基于卷积神经网络（CNN）和增强注意力模块的自动检测 COTS 的新方法。我们的数据集应用了不同的预训练 CNN 模型，即 VGG19 和 MobileNetV2，旨在通过迁移学习来检测和分类 COTS。使用 ADAM 优化器优化了预训练模型的架构，VGG19 达到了 87.1%的准确率，MobileNetV2 达到了 80.2%的准确率。开发了注意力模型并将其添加到 CNN 中，以确定海星中的哪些特征影响分类。增强后的模型的准确率达到了 92.6%，同时解释了 COTS 中的因果特征。在增强的 VGG-19 模型中添加注意力模型后的平均精度为 95%，与仅增强的 VGG-19 模型相比，增加了 2%。
