DEVELOPMENT OF EDGE CLOUD COLLABORATION TO INCREASE ACCURACY AND REDUCE LATENCY IN ROAD DAMAGE DETECTION
One important aspect of road maintenance is recognizing the type of road damage. Various studies have been carried out to identify various forms of road damage automatically. Automatic identification is generally carried out on the server, but this method has weakness due the latency. In this res...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/81153 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | One important aspect of road maintenance is recognizing the type of road
damage. Various studies have been carried out to identify various forms of road
damage automatically. Automatic identification is generally carried out on the
server, but this method has weakness due the latency. In this research, we utilize
edge-cloud collaboration to detect road damage. The dataset used is RDD2022.
This dataset will be trained with several algorithms, then we compare the
accuracy results to determine the best algorithm. We also carry out a
preprocessing stage with image enhancement to increase detection accuracy. This
image enhancement process is used to balance the dataset and add more features
to an image. This image enhancement process increases accuracy by 1.2%. Then,
we apply the selected model to be applied to edge devices to detect road damage.
Then images that are not successfully detected will be sent to the cloud to be
stored and re-trained. The model from this training will later be used to update
the existing model on the previous edge. Implementation of this edge-cloud
collaboration system results in faster detection time with a detection accuracy of
88.25%. |
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