2026-05-05T20:06:35.835Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:1:8554 2021-11-09T20:55:21Z 1

A generative model for the creation of a synthetic dataset for semantic segmentation 2021 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/1/8554 https://www.eventiotic.com/eventiotic/library/paper/641 M. Vidović Н. Нешић И. Радосављевић А. Митровић Ђ. Обрадовић The acquisition of large, annotated image datasets, required for the training of semantic segmentation models, is often an arduous task. This is because of the timeconsuming, complicated and error-prone nature of the process of manual image labelling. This process also often requires specialized software and domain knowledge. These problems can be circumvented by utilizing a generative model to create synthetic automatically labelled datasets. In this paper, we propose a generative model in the form of a 3D scene, representing an urban environment. A virtual camera setup is used to acquire labelled images from the virtual urban environment. Each image is stored as a multichannel EXR file, containing RGB data as well as an additional channel for each object class. These channels contain binary values which indicate whether a pixel belongs to the target class. These images are used to form a dataset for the training of semantic segmentation models. The viability of the generated dataset is evaluated by testing the trained semantic segmentation model on real world manually annotated images. conferenceObject 66 71 ICIST 2021 Proceedings