Semantička segmentacija kolničkih trakova

undergraduate thesis

• Title (english): Semantic segmentation of road lanes
• Author: Jelena Bratulić
• Mentor: Siniša Šegvić (mentor)
• Committee member: Siniša Šegvić (predsjednik povjerenstva)
• Committee member: Ivan Grubišić (član povjerenstva)
• Committee member: Petra Bevandić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2020-07-08, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Semantička segmentacija je jedan od problema kojim se bavi računalni vid. Semantička segmentacija ima širok spektar primjene, od medicine, poljoprivrede do u
posljednje vrijeme sve popularnijeg područja autonomne vožnje. Autonomna vožnja
nameće poseban izazov semantičkoj segmentaciji, posebice ukoliko se radi o segmentaciji
kolničkih trakova budući da su sami trakovi izrazito uski i teško primjetni na
slikama. Međutim, dobre rezultate u rješavanju tog problema postižu duboki konvolucijski
modeli s ljestvičastim naduzorkovanjem. U ovom radu implementiran je jedan
takav model te su provodeni i prikazani eksperimentalni rezultati na skupu podataka
LLAMAS.

Abstract (english)

Semantic segmentation is one of the problems that computer vision deals with.
Semantic segmentation has a wide range of applications, from medicine, agriculture
to the recently increasingly popular field of autonomous driving. Autonomous driving
imposes a special challenge on semantic segmentation, especially when it comes to
lane segmentation since the lanes themselves are extremely narrow and difficult to
notice in images. However, deep convolutional models with ladder upsampling achieve
good results in solving this problem. In this paper, one such model was implemented
and experimental results were performed and presented on the LLAMAS data set.

• Keywords: semantička segmentacija; računalni vid; autonomna vozila; duboki konvolucijski modeli; ljestvičasto naduzorkovanje; LLAMAS
• Keywords (english): semantic segmentation; computer vision; autonomus driving; deep convolutional models; ladder upsampling; LLAMAS
• Language: croatian
• URN:NBN: urn:nbn:hr:168:913204
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Repository: FER Repository
• Created on: 2023-01-14 08:34:38