基于混合优化策略的集成深度学习框架用于家蚕自动化健康检测与分析
Integrated Deep Learning Framework Designed on Hybrid Optimization Strategies for Automated Health Detection and Analysis in Silkworms
浏览论文内容
中文总结 AI 辅助
提出混合残差注意力网络（HRAN）结合集成自适应动量优化器（IAMO），实现家蚕图像六类健康与疾病状态的准确分类，准确率达98.67%，支持蚕业早期疾病检测。
中文摘要 AI 辅助
提出了一种混合残差注意力网络（Hybrid Residual Attention Network, HRAN），用于将家蚕图像准确分类为六种不同类别，包括健康状态和疾病状态。该网络利用残差块进行深层特征提取，并通过注意力机制聚焦于疾病相关特征。引入了一种新颖的集成自适应动量优化器（Integrated Adaptive Momentum Optimizer, IAMO），以增强收敛性并提高训练效率。家蚕图像数据集经过预处理技术，如归一化、调整大小和降噪，以及增强策略，以提高数据质量和多样性。使用IAMO进行优化后，该模型达到了98.67%的准确率。空间和通道注意力机制的整合，结合IAMO，显著增强了模型识别类别间细微差异的能力。结果表明，HRAN可用于在蚕业中早期检测疾病，未来的工作将增强其在不同环境中的可扩展性和效率。
英文摘要
A Hybrid Residual Attention Network is proposed for accurately classifying silkworm images into six different classes, including healthy and diseased states. It uses residual blocks for deep feature extraction and attention to focus on disease related features. A novel Integrated Adaptive Momentum Optimizer was introduced to enhance convergence and improve training efficiency. The dataset of silkworm images underwent preprocessing techniques such as normalization, resizing, and noise reduction, along with augmentation strategies to improve data quality and diversity. It is optimized using IAMO, achieved an accuracy of 98.67%.The integration of spatial and channel wise attention mechanisms, coupled with IAMO, significantly enhanced the model ability to recognize subtle differences between classes. Results indicate that HRAN can be used to detect disease at an early stage in sericulture, and future work will enhance scalability and efficiency in different environments.
发表机构
- University of Visvesvaraya College of Engineering（维斯瓦拉亚大学工程学院）
- Bangalore University（班加罗尔大学）
机构由 AI 辅助整理，请以论文原文为准。
