A Review of Neural Network based Semantic Segmentation for Scene Understanding in Context of the self driving Car Article Swipe

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Joshua Niemeijer , Paulin Pekezou Fouopi , Sascha Knake-Langhorst , Erhardt Barth ·

YOU? · · 2017 · Open Access ·

This paper tackles the challenge of scene understanding in context of automated driving. To react properly to the conditions given by the surrounding scene, the car has to understand it’s environment. Further the real time capability of a method solving this task is essential. \nFor scene understanding the car has to detect and classify surrounding objects. For this purpose one can employ a semantic segmentation to assign a class label to every pixel. \nIn this paper we evaluate the the state of the art methods for the semantic segmentation and perform tests on the FCN-8 architecture. Due to Hardware limitations, we train the FCN-8 on a downscaled version of the Cityscapes Dataset, containing urban traffic scenes. The evaluation of the results shows, the necessity to train the FCN-8 on the original size City Scapes Dataset. We conclude that we need to purchase a better hardware.

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Segmentation Computer science Task (project management) Artificial intelligence Context (archaeology) Class (philosophy) Computer vision Deep neural networks Artificial neural network Architecture State (computer science) Image segmentation Pixel Machine learning Geography Engineering Systems engineering Archaeology Algorithm

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Type: review
Language: en
PDF: https://elib.dlr.de/110862/1/PaperStudierendentagungJoshuaNiemeijer.pdf
OA Status: green
Cited By: 5
References: 5
Related Works: 20
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Title: A Review of Neural Network based Semantic Segmentation for Scene Understanding in Context of the self driving Car

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Language: en

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Publication year: 2017

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Publication date: 2017-01-01

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Authors: Joshua Niemeijer, Paulin Pekezou Fouopi, Sascha Knake-Langhorst, Erhardt Barth

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Concepts: Segmentation, Computer science, Task (project management), Artificial intelligence, Context (archaeology), Class (philosophy), Computer vision, Deep neural networks, Artificial neural network, Architecture, State (computer science), Image segmentation, Pixel, Machine learning, Geography, Engineering, Systems engineering, Archaeology, Algorithm

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Cited by: 5

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Citations by year (recent): 2023: 1, 2021: 1, 2020: 2, 2019: 1

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Related works (count): 20

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