Semantička segmentacija slika prirodnog krajolika temeljena na dubokom učenju

Božić-Štulić, Dunja

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doctoral thesis

Semantička segmentacija slika prirodnog krajolika temeljena na dubokom učenju

Split: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2020. urn:nbn:hr:179:179169

Božić-Štulić, Dunja

University of Split
Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture
Department of Electronics and Computing

Cite this document

APA 6th Edition

Božić Štulić, D. (2020). Semantička segmentacija slika prirodnog krajolika temeljena na dubokom učenju (Doctoral thesis). Split: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture. Retrieved from https://urn.nsk.hr/urn:nbn:hr:179:179169

MLA 8th Edition

Božić Štulić, Dunja. "Semantička segmentacija slika prirodnog krajolika temeljena na dubokom učenju." Doctoral thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, 2020. https://urn.nsk.hr/urn:nbn:hr:179:179169

Chicago 17th Edition

Harvard

Božić Štulić, D. (2020). 'Semantička segmentacija slika prirodnog krajolika temeljena na dubokom učenju', Doctoral thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, accessed 28 September 2023, https://urn.nsk.hr/urn:nbn:hr:179:179169

Vancouver

Božić Štulić D. Semantička segmentacija slika prirodnog krajolika temeljena na dubokom učenju [Doctoral thesis]. Split: University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture; 2020 [cited 2023 September 28] Available at: https://urn.nsk.hr/urn:nbn:hr:179:179169

IEEE

D. Božić Štulić, "Semantička segmentacija slika prirodnog krajolika temeljena na dubokom učenju", Doctoral thesis, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, Split, 2020. Available at: https://urn.nsk.hr/urn:nbn:hr:179:179169

Cite this item: https://urn.nsk.hr/urn:nbn:hr:179:179169

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Metadata

Title	Semantička segmentacija slika prirodnog krajolika temeljena na dubokom učenju
Title (english)	Semantic segmentation of natural landscape images based on deep learning
Author	Dunja Božić-Štulić https://orcid.org/0000-0001-5267-0848
Mentor	Darko Stipaničev (mentor)
Committee member	Vladan Papić (predsjednik povjerenstva)
Committee member	Sven Lončarić (član povjerenstva)
Committee member	Saša Mladenović (član povjerenstva)
Committee member	Damir Krstinić (član povjerenstva)
Committee member	Viktor Sučić (član povjerenstva)
Granter	University of Split Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture (Department of Electronics and Computing) Split
Defense date and country	2020-06-30, Croatia
Scientific / art field, discipline and subdiscipline	TECHNICAL SCIENCES Computing Artificial Intelligence
Universal decimal classification (UDC)	621.3 - Electrical engineering
Abstract	Semantička segmentacija slike značajni je zadatak u području računalnog vida i strojnog učenja. Ova doktorska disertacija bavi se razvojem nove metode za semantičku segmentaciju slika prirodnog krajolika. Modifikacijom bazne konvolucijske mreže predložen je novi model za semantičku segmentaciju slika. Pri izradi modela posebna pažnja je posvećena slojevima sažimanja, koji u modelima dubokog učenja predstavljaju veliki problem zbog gubitka podataka, koji je u kasnijim slojevima mreže nemoguće vratiti. Novi model ima segment posebno zadužen za rekonstrukciju izgubljenih podataka, pa je probleme gubitka podataka sveden na minimum u odnosu na postojeće mreže. Predložen je novi završni sloj koji poboljšava rekonstrukciju piksela slike izgubljenih u slojevima sažimanja. Evaluacija modela potvrdila je početne pretpostavke. Drugi dio ove disertacija je razvoj metode za proširenje referentne baze slika prirodnog krajolika i to posebno slika koje sadrže dim požara raslinja u početnim fazama gorenja. Predložen je i evaluiran model za generiranje realističkih sintetičkih slika dima požara raslinja primjenom generičkih suparničkih mreža. Ove su slike uspješno korištene u fazi treniranja mreže za semantičku klasifikaciju slika prirodnog krajolika i to posebno regija koje predstavljaju dim požara raslinja, što će značajno unaprijediti detekciju u automatskim sustavima za rano otkrivanje požara raslinja.
Abstract (english)	Semantic segmentation of regions on images is a significant task in the field of computer vision and machine learning. This doctoral dissertation deals with the development of a new method for the semantic segmentation of images of the natural landscape images. By modifying the base convolution network, a new model for semantic image segmentation is proposed. Particular attention was given to the compression layers, which in deep learning models pose a major data loss problem that is impossible to recover in later layers of the network. The new model has a segment specifically in charge of reconstructing lost data, so data loss problems are minimized over existing networks. A new final level is proposed that enhances the reconstruction of image pixels lost in the compression layers. Model evaluation confirmed the initial assumptions. The second part of this dissertation is the development of a method to extend the reference base of images of the natural landscape, especially images containing the wildfire smoke in the initial stages of burning. A model for generating realistic synthetic images of wildfire smoke using Generative Adversarial Network has been proposed and evaluated. These images have been successfully used in the training phase of the network for the semantic classification of the natural landscape images, especially the regions that represent wildfire smoke. This will probably significantly enhance detection in automated systems for early wildfire detection.
Keywords
Keywords (english)
Language	croatian
URN:NBN	urn:nbn:hr:179:179169
Promotion	2022
Study programme	Title: Electrical Engineering and Information Technology Study programme type: university Study level: postgraduate Academic / professional title: doktor/doktorica znanosti, područje tehničkih znanosti, polje elektrotehnika (doktor/doktorica znanosti, područje tehničkih znanosti, polje elektrotehnika)
Type of resource	Text
Extent	125 str.
File origin	Born digital
Access conditions	Access restricted to students and staff of home institution
Terms of use
Repository	Repository of the Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture
Created on	2022-02-28 10:47:07