用深度学习自动识别水下水母种类，准确率达98%。
Jellyfish Species Identification: A CNN Based Artificial Neural Network Approach
- 融合MobileNetV3等模型与神经网络，实现特征提取与分类一体化。
- 最佳模型准确率98%，显著优于其他组合方案。
- 适合海洋生态监测、生物多样性保护领域研究人员使用。
水母作为一类多样的凝胶状海洋生物，在维持海洋生态系统中起关键作用，但其快速繁殖和生态影响给生物多样性保护带来挑战。准确识别水母物种对生态监测与管理至关重要。本研究提出一种基于深度学习的水下图像水母物种检测与分类框架。该框架整合MobileNetV3、ResNet50、EfficientNetV2-B0、VGG16等先进特征提取技术，结合七种传统机器学习分类器及三种前馈神经网络分类器，实现精准物种识别。同时，通过激活softmax函数，直接利用卷积神经网络模型完成物种分类。其中，人工神经网络与MobileNetV3的组合表现最优，准确率达到98%，显著优于其他特征提取器-分类器组合。研究证明深度学习与混合框架在应对生物多样性挑战、推动海洋环境物种检测方面的有效性。
原文摘要 · Abstract (English)
Jellyfish, a diverse group of gelatinous marine organisms, play a crucial role in maintaining marine ecosystems but pose significant challenges for biodiversity and conservation due to their rapid proliferation and ecological impact. Accurate identification of jellyfish species is essential for ecological monitoring and management. In this study, we proposed a deep learning framework for jellyfish species detection and classification using an underwater image dataset. The framework integrates advanced feature extraction techniques, including MobileNetV3, ResNet50, EfficientNetV2-B0, and VGG16, combined with seven traditional machine learning classifiers and three Feedforward Neural Network classifiers for precise species identification. Additionally, we activated the softmax function to directly classify jellyfish species using the convolutional neural network models. The combination of the Artificial Neural Network with MobileNetV3 is our best-performing model, achieving an exceptional accuracy of 98%, significantly outperforming other feature extractor-classifier combinations. This study demonstrates the efficacy of deep learning and hybrid frameworks in addressing biodiversity challenges and advancing species detection in marine environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。