2.2. 4 Applications of Deep Learning Based Semantic Segmentation of Images
Journal
Enlargement and Integration Workshop
Date Issued
2022
Author(s)
Trajkovikj, Vladimir
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.
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.
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