Abstract: Pine wilt disease has posed a significant threat to forest ecosystems, due to its highly contagious and destructive nature. The critical step in the prevention and control of pine wilt is eliminating the disease sources, which requires the accurate recognition and removal of dead pine trees. However, small or blurred targets are captured, such as overexposure, backlight, and samples occluded by foliage in practical applications. The reason is that the UAVs have to fly high for capture, due to the geography of the hilly and mountain areas. In this study, a novel You Look Only Once v5 (YOLOv5) algorithm was proposed for the recognition of dead pine trees. The super-resolution reconstruction was performed at the feature level, in order to overcome the challenge of recognizing such targets. The YOLOv5 structure was redesigned in two aspects. Firstly, the Selective Kernel Feature Texture Transfer (SKFTT) module was adopted to create the high-resolution detection feature maps with detailed textures, where improved detection accuracy was obtained for the small targets and blurred targets. Specifically, the feature maps with high texture were selected from the backbone network, whereas, the feature maps with high semantics were selected from the feature fusion network. These feature maps were then sent to the texture extractor and content extractor. A selective feature fusion module was used to fuse the critical information about different scales using their weights. Secondly, the Foreground Background Loss function (FB Loss) was introduced to attenuate useless features, while enhancing the gradient contribution of positive samples, and balancing the distribution of positive and negative samples, in order to supervise the reconstruction of high-resolution feature maps. Furthermore, the dataset was obtained to validate the effectiveness of the improved model from the approximately 15 400 hectares of forest land located in Fuzhou and Minhou City, Fujian Province, China. The UAV images were subsequently cropped and screened to obtain about 29 250 labelled samples for further experiments. A series of ablation tests and visualizations were conducted on the testing datasets to verify the effectiveness. Experimental results showed that the mean Average Precision (mAP50) of the improved model was 92.7%, mAP50~95 was 62.1%, and APsmall was 53.2%. Compared with the baseline model, the improved model was achieved in the increases of 3.2, 8.3, and 15.8 percentage points in the mAP50, mAP50~95, and mAPsmall, respectively. The mAP50 of the improved model was 16.7, 15.3, 2.5, 2.8, 12.3, and 1.2 percentage points higher than that of the Faster R-CNN, YOLOv4, YOLOX, MT-YOLOv6, QueryDet, and DDYOLOv5 networks, respectively. In addition, the improved model was achieved in the frames per second FPS of 37, which fully met the detection requirements of dead pine trees. Visualization results showed that the improved model can be expected to serve as the recognition of occlusion, overexposure, and backlight targets. The feature maps of the small target detection layer were visualized with different super-resolution algorithms to facilitate observation. The comparison revealed that the texture was improved with the apparently clear boundary shape. In conclusion, the detecting challenge of small targets and blurred targets can be effectively alleviated using the improved dead pine tree detection algorithm, due to the high accuracy. Therefore, the improved detection algorithm is conducive to the efficient removal and comprehensive prevention/control of diseased trees. The improved model can greatly contribute to accelerating the “digital forest prevention” process in precision agriculture.