The Library
TCDNet : tree crown detection from UAV optical images using uncertainty-aware one-stage network
Tools
Wu, Weichao, Fan, Xijian, Qu, Hongyu, Yang, Xubing and Tjahjadi, Tardi (2022) TCDNet : tree crown detection from UAV optical images using uncertainty-aware one-stage network. IEEE Geoscience and Remote Sensing Letters, 19 . 6517405. doi:10.1109/LGRS.2022.3214281 ISSN 1545-598X.
|
PDF
WRAP-TCDNet-tree-crown-detection-UAV-optical-images-using-uncertainty-aware-one-stage-network-Tjahjadi-2022.pdf - Accepted Version - Requires a PDF viewer. Download (2510Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/LGRS.2022.3214281
Abstract
Tree crown detection plays a vital role in forestry management, resource statistics and yields forecasting. RGB high-resolution aerial images have emerged as a cost-effective source of data for tree crown detection. To address the challenges in the detection using UAV optical images, we propose a one-stage object detection network, TCDNet. First, the network provides an attention enhancement feature extraction module to enable the model to distinguish between tree crowns and their complex backgrounds. Second, an efficient loss is introduced to enable it to be aware of the overlap between adjacent trees, thus effectively avoiding misdetection. The experimental results on two publicly available datasets show that the proposed network outperforms state-of-art networks in terms of precision, recall and mean average precision.
Item Type: | Journal Article | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > QK Botany T Technology > TA Engineering (General). Civil engineering (General) |
|||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||
Library of Congress Subject Headings (LCSH): | Neural networks (Computer science), Crowns (Botany), Forest management, Optical images, Drone aircraft, Remote sensing | |||||||||
Journal or Publication Title: | IEEE Geoscience and Remote Sensing Letters | |||||||||
Publisher: | IEEE | |||||||||
ISSN: | 1545-598X | |||||||||
Official Date: | 13 October 2022 | |||||||||
Dates: |
|
|||||||||
Volume: | 19 | |||||||||
Article Number: | 6517405 | |||||||||
DOI: | 10.1109/LGRS.2022.3214281 | |||||||||
Status: | Peer Reviewed | |||||||||
Publication Status: | Published | |||||||||
Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||
Date of first compliant deposit: | 14 October 2022 | |||||||||
Date of first compliant Open Access: | 17 October 2022 | |||||||||
RIOXX Funder/Project Grant: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year