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

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...

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Bibliographic Details
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
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Summary: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.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics12112463