Development of an innovative and efficient urban vegetation monitoring system for sustainable urban ecology
type: CreativeWork
description: In the rapidly urbanizing environment of New Zealand, monitoring and preserving urban green spaces are crucial for ecological balance and sustainability. Urban vegetation significantly contributes to the ecological and social fabric of city life, making its distribution, health, and diversity essential for effective urban management and environmental protection. This research addresses a significant gap in current methodologies for monitoring urban greenery by introducing a more efficient, accurate, and less labor-intensive approach using deep learning technology. Extensive data collection, preprocessing, and training were key components of this research. Image collection routes were meticulously planned to ensure a comprehensive dataset representing diverse urban vegetation. A major challenge was the quality and privacy concerns within the dataset. Continuous image capturing in urban areas led to issues like blurry, repetitive, and nonvegetative images. To mitigate these issues, diverse filters were introduced to enhance the dataset's quality and ensure privacy protection. Focusing on advanced computer vision systems, this thesis employed the YOLOv5 and YOLOv8 models for automated urban vegetation detection using RGB images from car-mounted cameras. The models were chosen for their effectiveness in image recognition, with various image augmentation techniques and annotation tools enriching the training process. The thesis also explored transfer learning and layer freezing techniques in model training, along with a unique method of using larger bounding boxes for tree annotations to enhance the detection of specific tree species. The models' performance in vegetation detection was evaluated through precision and recall rates, highlighting their reliability and efficiency, with YOLOv5x demonstrating the highest precision (0.93) and recall (0.90) for urban vegetation detection. In tree species detection, the YOLOv5m with Transfer Learning model achieved a strong balan…
abstract: In the rapidly urbanizing environment of New Zealand, monitoring and preserving urban green spaces are crucial for ecological balance and sustainability. Urban vegetation significantly contributes to the ecological and social fabric of city life, making its distribution, health, and diversity essential for effective urban management and environmental protection. This research addresses a significant gap in current methodologies for monitoring urban greenery by introducing a more efficient, accurate, and less labor-intensive approach using deep learning technology. Extensive data collection, preprocessing, and training were key components of this research. Image collection routes were meticulously planned to ensure a comprehensive dataset representing diverse urban vegetation. A major challenge was the quality and privacy concerns within the dataset. Continuous image capturing in urban areas led to issues like blurry, repetitive, and nonvegetative images. To mitigate these issues, diverse filters were introduced to enhance the dataset's quality and ensure privacy protection. Focusing on advanced computer vision systems, this thesis employed the YOLOv5 and YOLOv8 models for automated urban vegetation detection using RGB images from car-mounted cameras. The models were chosen for their effectiveness in image recognition, with various image augmentation techniques and annotation tools enriching the training process. The thesis also explored transfer learning and layer freezing techniques in model training, along with a unique method of using larger bounding boxes for tree annotations to enhance the detection of specific tree species. The models' performance in vegetation detection was evaluated through precision and recall rates, highlighting their reliability and efficiency, with YOLOv5x demonstrating the highest precision (0.93) and recall (0.90) for urban vegetation detection. In tree species detection, the YOLOv5m with Transfer Learning model achieved a strong balan…
author: Alighaleh, Pejman
keywords: Urban Vegetation, Image Processing, Deep Learning, YOLO Series, Monitoring System
license: https://researchcommons.waikato.ac.nz/bitstreams/d21ebb43-79e3-4025-8598-77426a6ba399/download
date published: 2024
date modified: 2026-07-24T05:57:24Z
provider: University Waikato
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: The University of Waikato