A Shallow Pooled Weighted Feature Enhancement Network for Small-Sized Pine Wilt Diseased Tree Detection

• Published in: Electronics (Basel) 2023-05, Vol.12 (11), p.2463
• Main Authors: Yu, Mei, Ye, Sha, Zheng, Yuelin, Jiang, Yanjing, Peng, Yisheng, Sheng, Yuyang, Huang, Chongjing, Sun, Hang
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Datasets; Deep learning; Diseases and pests; Drone aircraft; Feature maps; Image enhancement; Object recognition; Pine; Plant diseases; Power; Recall; Remote sensing; Trees; Unmanned aerial vehicles
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics12112463

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.