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Related Experiment Video

Updated: Aug 29, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

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

Lijuan Shi¹, Guoying Wang¹, Lufeng Mo¹

¹College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China.

Sensors (Basel, Switzerland)

|September 9, 2022

Summary

This study introduces SEMD, a deep learning model for accurate semantic segmentation of standing trees. SEMD effectively segments trees in complex backgrounds, improving accuracy and reducing computational load.

Keywords:

attention mechanism deep learning semantic segmentation standing tree image

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Area of Science:

Computer Vision
Machine Learning
Forestry Technology

Background:

Accurate semantic segmentation of standing trees is crucial for automated data extraction in forestry.
Traditional methods often struggle with complex backgrounds and require manual intervention, limiting efficiency and accuracy.

Purpose of the Study:

To develop a lightweight deep learning model for accurate semantic segmentation of multiple standing trees, even in challenging environments.
To address the limitations of existing methods in terms of accuracy and computational complexity.

Main Methods:

Proposed SEMD (Semantic Segmentation Model) based on the DeepLabV3+ framework.
Integrated MobileNet for a lightweight backbone to reduce computational complexity.
Incorporated SENet (Squeeze-and-Excitation Network) attention mechanism for efficient feature extraction and suppression of irrelevant information.
Utilized multi-scale feature fusion to preserve image edge details and feature information.

Main Results:

Achieved high Mean Intersection over Union (MIoU) scores: 91.78% on simple backgrounds and 86.90% on complex backgrounds.
Demonstrated superior performance in segmenting diverse tree varieties and categories.
The lightweight nature of SEMD reduces computational demands compared to traditional models.

Conclusions:

The proposed SEMD model effectively solves the problem of segmenting multiple standing trees with high accuracy.
SEMD offers an efficient and accurate solution for automated tree factor extraction from images.
This deep learning approach advances the field of remote sensing and forest inventory management.