Automatic Segmentation of Standing Trees from Forest Images Based on Deep Learning

Semantic segmentation of standing trees is important to obtain factors of standing trees from images automatically and effectively. Aiming at the accurate segmentation of multiple standing trees in complex backgrounds, some traditional methods have shortcomings such as low segmentation accuracy and...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 22; no. 17; p. 6663
Main Authors: Shi, Lijuan, Wang, Guoying, Mo, Lufeng, Yi, Xiaomei, Wu, Xiaoping, Wu, Peng
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2022
MDPI
Subjects:
Accuracy
attention mechanism
Deep learning
Neural networks
semantic segmentation
Semantics
standing tree image
Trees
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Semantic segmentation of standing trees is important to obtain factors of standing trees from images automatically and effectively. Aiming at the accurate segmentation of multiple standing trees in complex backgrounds, some traditional methods have shortcomings such as low segmentation accuracy and manual intervention. To achieve accurate segmentation of standing tree images effectively, SEMD, a lightweight network segmentation model based on deep learning, is proposed in this article. DeepLabV3+ is chosen as the base framework to perform multi-scale fusion of the convolutional features of the standing trees in images, so as to reduce the loss of image edge details during the standing tree segmentation and reduce the loss of feature information. MobileNet, a lightweight network, is integrated into the backbone network to reduce the computational complexity. Furthermore, SENet, an attention mechanism, is added to obtain the feature information efficiently and suppress the generation of useless feature information. The extensive experimental results show that using the SEMD model the MIoU of the semantic segmentation of standing tree images of different varieties and categories under simple and complex backgrounds reaches 91.78% and 86.90%, respectively. The lightweight network segmentation model SEMD based on deep learning proposed in this paper can solve the problem of multiple standing trees segmentation with high accuracy.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22176663