Software and hardware.

The health of the trees in the forest affects the ecological environment, so timely detection of Standing Dead Trees (SDTs) plays an important role in forest management. However, due to the large spatial scope of forests, it is difficult to find SDTs through conventional approaches such as field inventories. In recent years, the development of deep learning and Unmanned Aerial Vehicle (UAV) has provided technical support for low-cost real-time monitoring of SDTs, but the inability to fully utilize global features and the difficulty of small-scale SDTs detection have brought challenges to the detection of SDTs in visible light images. Therefore, this paper proposes a multi-scale attention mechanism detection method for identifying SDTs in UAV RGB images. This method takes Faster-RCNN as the basic framework and uses Swin-Transformer as the backbone network for feature extraction, which can effectively obtain global information. Then, features of different scales are extracted through the feature pyramid structure and feature balance enhancement module. Finally, dynamic training is used to improve the quality of the model. The experimental results show that the algorithm proposed in this paper can effectively identify the SDTs in the visible light image of the UAV with an accuracy of 95.9%. This method of SDTs identification can not only improve the efficiency of SDTs exploration, but also help relevant departments to explore other forest species in the future.

森林树木的健康状况对生态环境至关重要，因此及时检测枯立木（Standing Dead Trees, SDTs）在森林经营管理中具有重要意义。然而，由于森林空间范围广阔，通过野外调查等常规手段难以高效发现枯立木。近年来，深度学习与无人机（Unmanned Aerial Vehicle, UAV）技术的发展为枯立木的低成本实时监测提供了技术支撑，但可见光影像中的枯立木检测仍面临两大挑战：无法充分利用全局特征，且小尺度枯立木的检测难度较高。为此，本文提出一种多尺度注意力机制检测方法，用于识别无人机RGB影像中的枯立木。该方法以Faster-RCNN为基础框架，采用Swin-Transformer作为骨干网络进行特征提取，可有效获取全局信息；随后通过特征金字塔结构与特征平衡增强模块提取多尺度特征；最后利用动态训练提升模型质量。实验结果表明，本文所提算法可有效识别无人机可见光影像中的枯立木，识别准确率达95.9%。该枯立木识别方法不仅可提升枯立木勘查效率，还可为未来相关部门开展其他森林物种研究提供助力。