Forest ecosystems are under constant threat from wood-boring pests such as the Emerald ash borer (EAB), which remain elusive owing to their hidden life cycles within tree trunks. Early detection is vital to mitigate economic and ecological damage. The main current monitoring method is manual detection which is ineffective at early stages of infestation. This study introduces VibroEABNet, a deep learning-based joint recognition network designed to enhance the detection of EAB boring vibration signals, with a novel approach integrating denoising and recognition modules.

中文翻译：

---

使用编码器-解码器和改进的 DenseNet 模型检测翡翠白蜡树蛀虫钻孔振动


森林生态系统一直受到翡翠白蜡树蛀虫 （EAB） 等蛀木害虫的威胁，由于它们的生命周期隐藏在树干中，因此仍然难以捉摸。早期发现对于减轻经济和生态损害至关重要。主要的电流监测方法是手动检测，这在侵扰的早期阶段是无效的。本研究引入了 VibroEABNet，这是一种基于深度学习的联合识别网络，旨在增强对 EAB 钻孔振动信号的检测，并采用了一种集成降噪和识别模块的新方法。