快速精确获取松材线虫病变色木的株数和位置信息,结合相应的治理措施是防止松材线虫病进一步蔓延的有效手段。通过无人机获取松材线虫病变色木林区的厘米级影像,采用SSD,YOLO v4和Faster R-CNN三种深度学习算法实现对变色木的自动检测。结果表明:相比YOLO v4和Faster R-CNN,SSD对于变色木的总体精度更高为75.0%;提出一种结合特征融合模块和通道注意力机制模块的方法改进SSD,改进SSD的总体检测精度为79.0%,相比SSD总体检测精度提升4.0%,表明改进SSD比SSD更适合变色木检测。验证区的变色木株数为87株,改进SSD正确检测株数为81株,检测的正确率高达93.1%,实现对林区无人机影像中松材线虫病变色木的精准检测,可为松材线虫病变色木的防治工作提供技术支持。

It is an effective means to prevent the further spread of pine wilt disease by quickly and accurately obtaining the number and location information of pine wilt discoloration wood and combining with the corresponding control measures.The centimeter-level image of pine wilted wood forest area was obtained by UAV,and the automatic detection of discoloration wood was realized by SSD,YOLO v4 and Faster R-CNN.The experimental results showed that compared with YOLO v4 and Faster R-CNN,the overall accuracy of SSD for discoloration wood was 75.0%.A method combining feature fusion module and channel attention mechanism module was proposed to improve SSD.The overall detection accuracy of improved SSD was 79.0%,which was 4.0% higher than that of SSD,indicating that the improved SSD was more suitable for discoloration wood detection than SSD.In addition,the number of discoloration trees in the verification area was 87,and the number of correct detection trees in the improved SSD was 81.The accuracy of detection was as high as 93.1%.The accurate detection of pine wilt discoloration trees in UAV images of forest areas was realized,which can provide technical support for the prevention and control of pine wilt discoloration trees.