Pine wilt disease is extremely ruinous to forests. It is an important to hold back the transmission of the disease in order to detect diseased trees on UAV imagery, by using a detection algorithm. However, most of the existing detection algorithms for diseased trees ignore the interference of complex backgrounds to the diseased tree feature extraction in drone images. Moreover, the sampling range of the positive sample does not match the circular shape of the diseased tree in the existing sampling methods, resulting in a poor-quality positive sample of the sampled diseased tree. This paper proposes a Global Multi-Scale Channel Adaptation Network to solve these problems. Specifically, a global multi-scale channel attention module is developed, which alleviates the negative impact of background regions on the model. In addition, a center circle sampling method is proposed to make the sampling range of the positive sample fit the shape of a circular disease tree target, enhancing the positive sample’s sampling quality significantly. The experimental results show that our algorithm exceeds the seven mainstream algorithms on the diseased tree dataset, and achieves the best detection effect. The average precision (AP) and the recall are 79.8% and 86.6%, respectively.

Pine wild disease poses a serious threat to the ecological environment of national forests. Combining the object detection algorithm with Unmanned Aerial Vehicles (UAV) to detect pine wild diseased trees (PWDT) is a significant step in preventing the spread of pine wild disease. To address the issue of shallow feature layers lacking the ability to fully extract features from small-sized diseased trees in existing detection algorithms, as well as the problem of a small number of small-sized diseased trees in a single image, a Shallow Pooled Weighted Feature Enhancement Network (SPW-FEN) based on Small Target Expansion (STE) has been proposed for detecting PWDT. First, a Pooled Weighted Channel Attention (PWCA) module is presented and introduced into the shallow feature layer with rich small target information to enhance the network’s expressive ability regarding the characteristics of two-layer shallow feature maps. Additionally, an STE data enhancement method is introduced for small-sized targets, which effectively increases the sample size of small-sized diseased trees in a single image. The experimental results on the PWDT dataset indicate that the proposed algorithm achieved an average precision and recall of 79.1% and 86.9%, respectively. This is 3.6 and 3.8 percentage points higher, respectively, than the recognition recall and average precision of the existing state-of-the-art method Faster-RCNN, and 6.4 and 5.5 percentage points higher than those of the newly proposed YOLOv6 method.