Pine wilt disease (PWD) is a devastating infectious disease for the rapid spread, short disease period, and strong pathogenic ability. At present, detecting the PWD induced dead pine trees (DPT) timely and then taking corresponding measures are vital to control the spread of PWD. An unmanned aerial vehicle (UAV) platform equipped with the Vis-RGB digital camera was used to obtain the ultra-high spatial resolution images. Deep learning object detection of Faster R-CNN was adopted to detect the DPT automatically. Different from the previous research on the DPT identification, the influences of other dead trees and red broadleaved trees on DPT identification were considered. The results showed that Faster R-CNN can effectively identify the DPT. The 6.78 percentage points detection accuracy of the DPT would be improved when taking the anchor size, other dead trees and red broad-leaved trees into consideration. The overall accuracy of DPT detection can reach 82.42%, which can meet the protector for felling of the DPT. Finally, the position of predicted DPT was calculated accurately using coordinate transformation. Combined with the point combination process, 494 DPT were correctly located. This research had the advantages of low cost, high efficiency and automatic identification, and can provide technical support for the prevention and control of PWD. The combination of UAV remote sensing and object detection algorithms was a promising method to monitor the occurrence of PWD and the distribution of the DPT, which provided important basis for the consequence harmless treatment of PWD induced DPT.