Urban scene semantic segmentation using the U-Net model

• Autorzy: Ciecholewski Marcin

Identyfikatory

DOI

10.15439/2023F3686

• Języki publikacji: EN

Abstrakty

EN

Vision-based semantic segmentation of complex urban street scenes is a very important function during autonomous driving (AD), which will become an important technology in industrialized countries in the near future. Today, advanced driver assistance systems (ADAS) improve traffic safety thanks to the application of solutions that enable detecting objects, recognising road signs, segmenting the road, etc. The basis for these functionalities is the adoption of various classifiers. This publication presents solutions utilising convolutional neural networks, such as MobileNet and ResNet50, which were used as encoders in the U-Net model to semantically segment images of complex urban scenes taken from the publicly available Cityscapes dataset. Some modifications of the encoder/decoder architecture of the U-Net model were also proposed and the result was named the MU-Net. During tests carried out on 500 images, the MU-Net model produced slightly better segmentation results than the universal MobileNet and ResNet networks, as measured by the Jaccard index, which amounted to 88.85%. The experiments showed that the MobileNet network had the best ratio of accuracy to the number of parameters used and at the same time was the least sensitive to unusual phenomena occurring in images.

Słowa kluczowe

EN

semantic segmentation; road; neural network; vegetation mapping; mobile phone; safety

PL

segmentacja semantyczna; droga; sieć neuronowa; mapowanie; roślinność; telefon komórkowy; bezpieczeństwo

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2023
• Tom: Vol. 35
• Strony: 907--912
• Opis fizyczny: Bibliogr. 22 poz., il., tab.

Twórcy

autor

Ciecholewski Marcin

• Department of Geoinformatics Faculty of Electronics, Telecommunication and Informatics Gdańsk University of Technology Gdańsk, Poland

Bibliografia

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Uwagi

1. Thematic Tracks Short Papers

2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).