diplomsko delo
Matej Miočić (Author), Matej Kristan (Mentor)

Abstract

Avtonomna plovila se zanašajo na robustne metode zaznavanja ovir. Najsodobnejše metode temeljijo na segmentacijskih mrežah, ki so naučene na velikih naborih podatkov. Ker je nabor realnih slik zelo omejen, ročna segementacija pa časovno zahtevna in podvržena človeškim napakam, predlagamo alternativo –- izdelavo simulacijskih slik s samodejno segmentacijo. V nalogi predlagamo simulacijsko okolje za generiranje simuliranih vodnih scen in njihovih segmentacijskih mask. Analizirane so možnosti izboljšave segmentacijskih mrež za detekcijo ovir na vodi preko uporabe generiranih simuliranih vodnih scen. Predstavljamo primerjavo rezultatov učenja segmentacijske mreže na zbirki realnih slik z rezultati učenja mrež na zbirki simuliranih vodnih scen ter z rezultati učenja mrež na kombinaciji obeh zbirk. Rezultati analize kažejo, da je F-mera znotraj nevarnega območja z mrežami, ki so naučene na kombinaciji obeh zbirk, za 5 % višja kot z mrežami, ki so trenirane na zbirki brez sintetičnih slik, F-mera za celotno območje pa je za 0,3 % višja.

Keywords

simulacijsko okolje;semantična segmentacija;Blender;samodejno generirani sintetični podatki;samovozeča vozila;računalništvo in informatika;univerzitetni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [M. Miočić]
UDC: 004.93:004.94(043.2)
COBISS: 77865731 Link will open in a new window
Views: 280
Downloads: 36
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Other data

Secondary language: English
Secondary title: Using simulated scenes for improving water surface obstacle detection
Secondary abstract: Autonomous vessels rely on robust obstacle detection methods. State-of-the-art methods are based on segmentation networks that are trained on large datasets. Since the dataset of real images is very limited, and manual segmentation is time-consuming and subject to human error, we suggest an alternative -- the creation of simulation images with automatic segmentation. In this paper, we propose a simulation environment for generating simulated water scenes and their segmentation masks. Possibilities of improving segmentation networks for detection of obstacles on water through the use of generated simulated water scenes are analyzed. We present a comparison of the results of training the segmentation network on a dataset of real images with the results of training the networks on the dataset of simulated water scenes and with the results of training the networks on a combination of both datasets. The results of the analysis show that the F-measure within the danger zone with networks learned on the combination of both datasets is 5 % higher than with networks trained on the dataset without synthetic images, and the F-measure for the whole area is 0.3 % higher.
Secondary keywords: simulation environment;computer vision;semantic segmentation;Blender;automatically generated synthetic data;self-driving vehicles;computer science;computer and information science;diploma;Računalniški vid;Računalniška simulacija;Vodna plovila;Računalništvo;Univerzitetna in visokošolska dela;
Type (COBISS): Bachelor thesis/paper
Study programme: 1000468
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 52 str.
ID: 13394700