Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/22269
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lameski, Petre | en_US |
dc.contributor.author | Zdravevski, Eftim | en_US |
dc.contributor.author | Kulakov, Andrea | en_US |
dc.contributor.author | Chorbev, Ivan | en_US |
dc.contributor.author | Trajkovikj, Vladimir | en_US |
dc.date.accessioned | 2022-08-15T09:32:56Z | - |
dc.date.available | 2022-08-15T09:32:56Z | - |
dc.date.issued | 2022 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.12188/22269 | - |
dc.description.abstract | Deep convolutional neural network is demonstrated on two problems: semantic segmentation of agricultural images for weed detection and semantic segmentation of garbage in images. Weed segmentation is important since it allows detection of weed infestation in agricultural plantations and enables farmers to perform targeted herbicide application. Garbage detection is important to create applications that would allow easier reporting of littered sites to the authorities and increase the public awareness about the problem. Using transfer learning methods improved the model accuracy for weed segmentation, and showed great potential for application of this method using cheap sensors on farms. The algorithm for garbage detection achieved high accuracy for classification of different garbage types, allowing the potential deployment of this system on cloud network. | en_US |
dc.relation.ispartof | Enlargement and Integration Workshop | en_US |
dc.title | 2.2. 4 Applications of Deep Learning Based Semantic Segmentation of Images | en_US |
dc.type | Article | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
crisitem.author.dept | Faculty of Computer Science and Engineering | - |
crisitem.author.dept | Faculty of Computer Science and Engineering | - |
crisitem.author.dept | Faculty of Computer Science and Engineering | - |
Appears in Collections: | Faculty of Computer Science and Engineering: Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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JRC129903_01.pdf | 3.16 MB | Adobe PDF | View/Open |
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