Detection Method of Infected Wood on Digital Orthophoto Map–Digital Surface Model Fusion Network

Abstract

Pine wilt disease (PWD) is a worldwide affliction that poses a significant menace to forest ecosystems. The swift and precise identification of pine trees under infection holds paramount significance in the proficient administration of this ailment. The progression of remote sensing and deep learning methodologies has propelled the utilization of target detection and recognition techniques reliant on remote sensing imagery, emerging as the prevailing strategy for pinpointing affected trees. Although the existing object detection algorithms have achieved remarkable success, virtually all methods solely rely on a Digital Orthophoto Map (DOM), which is not suitable for diseased trees detection, leading to a large false detection rate in the detection of easily confused targets, such as bare land, houses, brown herbs and so on. In order to improve the ability of detecting diseased trees and preventing the spread of the epidemic, we construct a large-scale PWD detection dataset with both DOM and Digital Surface Model (DSM) images and propose a novel detection framework, DDNet, which makes full use of the spectral features and geomorphological spatial features of remote sensing targets. The experimental results show that the proposed joint network achieves an AP50 2.4% higher than the traditional deep learning network.

Detection of the Infection Stage of Pine Wilt Disease and Spread Distance Using Monthly UAV-Based Imagery and a Deep Learning Approach

“…Regarding the middle stage, infected trees with a mixture of green and red needles were easily misidentified as late-stage trees and healthy trees, resulting in a lower detection accuracy [55]. The lowest accuracy in the early stage was obtained due to the similar spectral characteristics of infected tree crowns and healthy tree crowns, which has been demonstrated by previous studies [54,56]. In addition, topographic relief causes differences in the spatial resolution of tree crowns across the entire scene, resulting in uncertainty in the model's extraction of tree crown features at different stages, which will affect the accuracy of PWD detection.…”

“…Regarding the middle stage, infected trees with a mixture of green and red needles were easily misidentified as late-stage trees and healthy trees, resulting in a lower detection accuracy [55]. The lowest accuracy in the early stage was obtained due to the similar spectral characteristics of infected tree crowns and healthy tree crowns, which has been demonstrated by previous studies [54,56]. In…”

“…Additionally, the backbone network based on ResNet-50 leads to a deeper structure and a larger number of parameters, increasing the model's complexity and computation cost (Table 4), resulting in slower convergence. YOLOv5 adopts a single-stage and end-to-end detection framework with a simplified and efficient network structure, offering advantages in detecting PWD at the canopy level [50][51][52][53][54]. YOLOv8, which has a similar framework, adopts a more complex C2f structure, but its accuracy is slightly lower than YOLOv5 (Table 4 and Figure 7).…”