Year: 2022
Type: Article
Title: 2.2. 4 Applications of Deep Learning Based Semantic Segmentation of Images
Author: Lameski, Petre
Author: Zdravevski, Eftim
Author: Kulakov, Andrea
Author: Chorbev, Ivan
Author: 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.
Publisher:
Relation: Enlargement and Integration Workshop
Identifier: oai:repository.ukim.mk:20.500.12188/22269
Identifier: http://hdl.handle.net/20.500.12188/22269
