Automatic Detection and Parameter Estimation of Ginkgo biloba in Urban Environment Based on RGB Images. (6th August 2021)

• Record Type: Journal Article
• Authors: Xia, Kai; Wang, Hao; Yang, Yinhui; Du, Xiaochen; Feng, Hailin
• Other Names: Hongxiao Liu Academic Editor.
• Is Part Of: Journal of sensors. Volume 2021(2021)
• Publication Date: 2021-08-06
• Subjects: Detectors -- Periodicals; 681.205
• DOI: 10.1155/2021/6668934
• Languages: English
• ISSNs: 1687-725X

Abstract

Abstract : Individual tree crown detection and morphological parameter estimation can be used to quantify the social, ecological, and landscape value of urban trees, which play increasingly important roles in densely built cities. In this study, a novel architecture based on deep learning was developed to automatically detect tree crowns and estimate crown sizes and tree heights from a set of red-green-blue (RGB) images. The feasibility of the architecture was verified based on high-resolution unmanned aerial vehicle (UAV) images using a neural network called FPN-Faster R-CNN, which is a unified network combining a feature pyramid network (FPN) and a faster region-based convolutional neural network (Faster R-CNN). Among more than 400 tree crowns, including 213 crowns of Ginkgo biloba, in 7 complex test scenes, 174 ginkgo tree crowns were correctly identified, yielding a recall level of 0.82. The precision and F -score were 0.96 and 0.88, respectively. The mean absolute error (MAE) and mean absolute percentage error (MAPE) of crown width estimation were 0.37 m and 8.71%, respectively. The MAE and MAPE of tree height estimation were 0.68 m and 7.33%, respectively. The results showed that the architecture is practical and can be applied to many complex urban scenes to meet the needs of urban green space inventory management.