松材线虫病对全球松树资源和生态环境造成严重威胁,准确检测疫木对于控制疫情蔓延具有重要意义。借助无人机遥感技术,能够高效获取覆盖广泛、分辨率高的森林影像数据,为松材线虫病疫木检测提供了关键的数据支持。针对复杂森林环境下无人机遥感影像中松材线虫病疫木检测识别能力受限的问题,提出一种改进的YOLOv8n检测模型(YOLOv8n-RCD)。使用RepVit作为主干网络,提升特征提取能力;通过引入跨尺度特征融合模块(CCFM),增强模型对多层次特征的提取;采用动态头(Dynamic Head)替换原有检测头,提高模型在复杂背景下的目标识别能力和适应性。结果表明:改进后的YOLOv8n-RCD在精确率(P)、召回率(R)和F₁分数上比基准模型(YOLOv8n)分别提升了3.37%、3.00%和3.19%,AP50和AP50-95分别提升了1.93%和1.49%。改进后的模型提升了在复杂森林环境下的识别精度和能力,为松材线虫病疫木的精确化检测和无人机遥感驱动的智能化动态防控提供了有力的技术支持。

Pine wilt disease poses a serious threat to global pine resources and ecological environment.Accurate detection of infected trees is critical to prevent further spread of the disease.We used unmanned aerial vehicle(UAV)-based remote sensing technology for the efficient acquisition of extensive and high-resolution imagery of forested areas,providing crucial data support for the detection of PWD-infected pine trees.To address the limitation in detection capability of PWD-infected pine trees within UAV remote sensing imagery under complex forest environments,we presented an enhanced YOLOv8n detection model called YOLOv8n-RCD.The model employs RepVit as the backbone network to improve feature extraction capability,integrates a Cross-scale Convolutional Feature Fusion Module(CCFM)to strengthen multi-level feature extraction,and employs Dynamic Head in place of the original detection head,thereby enhancing target recognition and adaptability in complex backgrounds.Experimental evaluations demonstrated that the improved model of YOLOv8n-RCD achieved relative gains of 3.37%,3.00%,and 3.19% in precision(P),recall(R),and F₁ score,respectively,over the baseline model YOLOv8n,and its AP50 and AP50-95 were increased by 1.93% and 1.49% compared with the latter.The enhanced model improved detection accuracy and recognition capability in complex forest environments,providing robust technical support for precise identification and UAV remote sensing-based intelligent monitoring of PWD-infected pine trees.