采用多阶段深度学习与 grad-CAM 可解释性以及孟加拉国北部的整体技术来对本土木种进行分类
Ashrafun Zannat1,2, Md Saiful Islam1, Md Shahriar Zaman2
1Institute of Information and Communication Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.
PloS one
|July 29, 2025
概括
本研究介绍了BdWood模型,用于通过深度学习识别孟加拉国木材物种. 布德伍德模型实现了高精度,优于其他林业和保护方法.
科学领域:
- 林业 林业 林业 林业 林业
- 计算机科学 计算机科学
- 生态保护 生态保护
背景情况:
- 对于林业和保护工作而言,木材物种的认可至关重要.
- 现有的方法往往专注于特定地区,需要本地化解决方案.
- 为孟加拉国木材物种开发了一个专门的图像数据集.
研究的目的:
- 将一个基于CNN的新型顺序卷积神经网络模型 (BdWood) 与各种深度学习,集合和机器学习模型进行比较.
- 评估模型在特定于孟加拉国森林的树木物种识别方面的性能.
- 为木材物种分类提供实际应用.
主要方法:
- 开发一个定制的数据集,包含7119多张孟加拉七种木种的高质量图像.
- 实施和比较七个预训练深度学习模型,包括DenseNet121.1.
- 开发和评估一个名为BdWood的基于CNN的序列模型.
- 使用梯度加权类激活映射 (Grad CAM) 来实现模型的可解释性.
- 为实际使用开发了一个Android应用程序.
主要成果:
- 在预训练模型中,DenseNet121的准确度最高,为97.09%.
- 定制的BdWood模型表现出卓越的性能,训练准确率为99.80%,验证准确率为97.93%.
- BdWood获得了97.94%的F1分数和99.85%的曲线下的接收器操作特征面积 (ROC-AUC).
- 格拉德CAM提供了关于特征对分类的重要性的见解.
结论:
- 在孟加拉国森林中,BdWood模型对木种的分类非常有效.
- 深度学习方法,特别是CNN,对这个任务有很大的前景.
- 开发的Android应用程序有助于研究结果在现实世界中的实际应用.
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