Abstract: Abstract: The unmanned aerial vehicle (UAV) remote sensing featured by low cost and flexibility offers a promising solution for pests monitoring by acquiring high resolution forest imagery. So the forest pest monitoring system based on UAV is essential to the early warning of red turpentine beetle (RTB) outbreaks. However, the UAV monitoring method based on image analysis technology suffers from inefficiency and depending on pre-processing, which prohibits the practical application of UAV remote sensing. Due to the long process flow, traditional methods can not locate the outbreak center and track the development of epidemic in time. The RTB is a major forestry invasive pest which damages the coniferous species of pine trees in northern China. This paper focuses on the detection of pines infected by RTBs. A real-time forest pest monitoring method based on deep learning is proposed for UAV forest imagery. The proposed method was consisted of three steps: 1) The UAV equipped with prime lens camera scans the infected forest and collects images at fixes points. 2) The Android client on UAV remote controller receives images and then requests the mobile graphics workstation for infected trees detection through TensorFlow Serving in real time. 3) The mobile graphics workstation runs a tailored SSD300 (single shot multibox detector) model with graphics processing unit (GPU) parallel acceleration to detect infected trees without orthorectification and image mosaic. Compared with Faster R-CNN and other two-stage object detection frameworks, SSD, as a lightweight object detection framework, shows the advantages of real-time and high accuracy. The original SSD300 object detection framework uses truncated VGG16 as basic feature extractor and the 6 layers (named P1-P6) prediction module to detect objects with different sizes. The proposed tailored SSD300 object detection framework includes two parts. First, a 13-layer depthwise separable convolution is used as basic feature extractor, which reduces several times computation overhead compared with the standard convolutions in VGG16. Second, most loss is derived from positive default boxes and these boxes mainly concentrated in P2 and P3 due to the constraints of crown size, UAV flying height and lens' focal length. Therefore, the tailored SSD300 retains only P2 and P3 as prediction module and the other prediction layers are deleted to further reduce computation overhead. Besides, aspect ratio of default boxes is set to 1, 2, 1/2, since the aspect ratio of crown is approximate 1. The UAV imagery is collected on 6 experimental plots at 50-75 m height. The photos of No.2 experimental plot are considered as test set and the rest are train set. A total of 82 aerial photos are used in the experiment, including 70 photos in the train set and 12 photos in the test set. The AP and run time of five models are evaluated. The average precision (AP) of the tailored SSD300 model reaches up to 97.22%, which is lower than the AP of original SSD300. While the proposed model has only 18.8 MB parameters, reducing above 530 MB compared with the original model. And the run time is 0.46 s on a mobile workstation equipped with NVIDIA GTX 1050Ti GPU, while the original model needs 4.56 s. Experimental results demonstrate that the downsize of basic feature extractor and prediction module speed up detection with a little impact on AP. The maximum coverage of aerial photo captured at 75 m height is 38.18 m×50.95 m. When the UAV has a horizontal speed of 15 m/s, it takes 3.4 s to move to the next shooting point without overlap, longer than the detection time. Therefore, the proposed method can simplify the detection process of UAV monitoring and realizes the real-time detection of RTB damaged pines, which introduces a practical and applicable solution for early warning of RTB outbreaks.