Metoda za procjenu dubine scene korištenjem jedne slike

master's thesis

• Title (english): A Method for Scene Depth Estimation Using Single Image
• Author: Gabriela Mišković
• Mentor: Sven Lončarić (mentor)
• Committee member: Sven Lončarić (predsjednik povjerenstva)
• Committee member: Marko Subašić (član povjerenstva)
• Committee member: Tomislav Petković (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2021-07-09, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Razvoj računarskih znanosti uvijek je bio popraćen razvojem tehnologije. Sve češća upotreba modernih vozila zahtjeva visoke tehnološke standarde. Upravo je računalni vid grana koja uvelike pronalazi primjenu u tehnološkom napretku modernih vozila. Važan zadatak koji rješava računalni vid je procjena dubine scene. Korištenje podataka o dubini vozilima omogućuje izbjegavanje sudara s objektima za koje se utvrdi da su preblizu. Na taj način se može drastično utjecati na smanjenje prometnih nesreća i samim time spasiti mnoge živote.

Cilj ovog rada bio je napraviti vlastito rješenje na temelju postojeće metode za procjenu dubine scene korištenjem samo jedne slike. U skladu s postavljenim ciljem, u radu su predstavljene umjetne neuronske mreže s posebnim naglaskom na duboke konvolucijske neuronske mreže. Opisan je korišteni kitti set podataka, kao i detalji implementacije same metode te su predstavljeni dobiveni rezultati.

Abstract (english)

The development of computer science has always been followed up by the development of technology. The increasing use of modern vehicles requires high technological standards. Computer vision is part of artificial intelligence which has a wide use in technological advancement of the modern vehicles. The important task solved by computer vision is depth estimation of the scene. Usage of depth data allows vehicles to avoid collisions with objects that are believed to be too close, and that is how number of traffic collisions may be drastically reduced and many lives saved.

The aim of this paper was to create my own solution based on existing monodepth method. In addition to the subject, this paper presents artificial neural networks with special accent on deep convolutional neural networks. Used kitti data set is described, as well as the details of implementation of the method itself, and the obtained results are presented.

• Keywords: monokularna procjena dubine; neuronske mreže; konvolucijske neuronske mreže; duboko učenje; računalni vid; kitti
• Keywords (english): monodepth; neural networks; convolutional neural networks; deep learning; computer vision; kitti
• Language: croatian
• URN:NBN: urn:nbn:hr:168:314880
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-01-13 15:40:01