DEVELOPMENT OF TREE DETECTOR MODEL AND WEB CLIENT FOR ARTIFICIAL INTELLIGENCE SYSTEM TO DETECT TREES WITH THE POTENTIAL TO DAMAGE POWER LINE
According to data from Unison Group New Zealand, 20% of unwanted power outages occur due to vegetation growing too close to power lines. In Indonesia itself, there has been a case of blackout caused by the Sengon tree growing through the Right of Way (ROW) of electricity network. One of the cau...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/55557 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | According to data from Unison Group New Zealand, 20% of unwanted power
outages occur due to vegetation growing too close to power lines. In Indonesia
itself, there has been a case of blackout caused by the Sengon tree growing through
the Right of Way (ROW) of electricity network. One of the causes of this incident
was the difficulty in managing the vegetation around the power line, especially in
remote areas. An Artificial Intelligence (AI) system to detect trees that have the
potential to harm the power line was developed as a solution to this problem. By
using object detection technology, AI will detect trees and power lines in the image
from the UAV and satellite imagery. Then, AI will calculate the distance between
the trees and the power line, then assign a box with a certain color to each tree
according to the calculated distance. This AI is integrated with a web client, so that
users can easily access this AI.
The focus of this final project is the development of a tree detection model and a
web client. Development is carried out by conducting literature studies, design
making, implementation, and testing. Tree detection will be performed using the
DeepForest library. Transfer learning from the DeepForest prebuilt model will be
performed on the associated data to optimize tree detection. The performance of
tree detection model will be measured by mean Average Precision (mAP)
parameter. First, the prebuilt model will be tested with varying patch size
parameter to determine a good patch size value to use. Then, transfer learning is
carried out and the performance of each resulting model will be compared. The
model with the best performance will be selected. Web client testing will be carried
out by functionality test and Technology Acceptance Model (TAM) survey. In the
survey, users will judge how easy to use and informative the web client is.
From this research, patch size values that can be used to produce a good tree
detection, a better model for detecting trees from satellite imagery, and a web client
with proven functionality and convenience are obtained. |
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