Test result.

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.

森林中树木的健康状况直接影响生态环境，因此及时检测枯立木（Standing Dead Trees, SDTs）对于森林经营管理具有重要意义。然而，森林空间范围广阔，采用野外调查等传统方法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，但可见光图像场景下的枯立木检测仍面临两大挑战：一是无法充分利用全局特征，二是小尺度枯立木的检测难度较高。为此，本文提出一种面向无人机RGB图像中枯立木识别的多尺度注意力机制检测方法。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；最后借助动态训练提升模型性能。实验结果表明，本文所提算法可有效识别无人机可见光图像中的枯立木，识别准确率达95.9%。该枯立木识别方法不仅能够提升枯立木排查效率，还可为后续相关部门开展其他森林物种研究提供助力。