Temporal and contextual aggregation for road marking semantic segmentation

Hiroaki Aizawa; Yuto Ura; Kunihito Kato

doi:10.1117/12.2566897

1 June 2020 Temporal and contextual aggregation for road marking semantic segmentation

Hiroaki Aizawa, Yuto Ura, Kunihito Kato

Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 115150E (2020) https://doi.org/10.1117/12.2566897
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia

Abstract

In this paper, we propose convolutional neural networks for semantic segmentation on road markings in the situation where sequential segmentation ground truth masks are available. The proposed model aggregates the temporal information and the context information from the multiple frames. Moreover, we employ CGNet as the backbone network to reduce trainable parameters and computation speed. In the experiment, we evaluate the model using the Gifu-city Road Marking Segmentation Dataset, which includes road markings of open roads in Gifu city. As a result, the segmentation performance such as a white center line and white dash line is an improvement.

Citation Download Citation

Hiroaki Aizawa, Yuto Ura, and Kunihito Kato "Temporal and contextual aggregation for road marking semantic segmentation", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 115150E (1 June 2020); https://doi.org/10.1117/12.2566897

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KEYWORDS

Roads

Image segmentation

Convolution

3D modeling

Video

Feature extraction

Convolutional neural networks

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