VGU RESEARCH REPOSITORY
Please use this identifier to cite or link to this item:
https://epub.vgu.edu.vn/handle/dlibvgu/1879| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Ngo Phuc Linh | en_US |
| dc.date.accessioned | 2024-11-18T06:53:24Z | - |
| dc.date.available | 2024-11-18T06:53:24Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | https://epub.vgu.edu.vn/handle/dlibvgu/1879 | - |
| dc.description.abstract | Larvae source management (LSM) is a control strategy for detecting and controlling mosquito populations by targeting the immature stages of mosquitoes, i.e., larvae. The identification of water bodies would play a crucial role and signification method in controlling mosquito populations. Implementing Unmanned aerial vehicles (UAV) for identifying breeding sites would be considered as a practical solution to address the problem. In my thesis, I would like to present a model for detecting temporary water bodies taken by drone-captured images collected in the Vietnamese-German University area and Binh Duong province. For my experiments, I use an RGB dataset image which includes temporary water bodies labeled a bounding box by the LabelImg application and then training and detecting by YOLOv7 algorithm. My result shows that the YOLOv7 algorithm trained models could discriminate the appearance of water bodies where mosquitoes could be bred with overall average accuracy mean Average Precision (mAP) over five times training is 69.2%. The model also labels water bodies’ imagery to create a dataset for training computer vision models and could be applied for further research | en_US |
| dc.language.iso | en | en_US |
| dc.rights | Attribution-NonCommercial 4.0 International | * |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | * |
| dc.subject | Temporary water bodies | en_US |
| dc.subject | Unmanned aerial vehicles | en_US |
| dc.subject | YOLOv7 algorithm | en_US |
| dc.subject | Object detection | en_US |
| dc.title | Automatic detection of water bodies with potential mosquito larvae ground from image captured by an UAV drove | en_US |
| dc.type | Thesis | en_US |
| item.fulltext | With Fulltext | - |
| item.languageiso639-1 | other | - |
| item.grantfulltext | restricted | - |
| Appears in Collections: | Computer Science (CS) | |
Files in This Item:
| File | Description | Size | Format | Existing users please Login |
|---|---|---|---|---|
| Automatic detection of water bodies with potential mosquito larvae ground from image captured by an UAV drove.pdf | 12.58 MB | Adobe PDF |
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