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Open AccessArticle

Real-Time Semantic Segmentation for Fisheye Urban Driving Images Based on ERFNet

Electronics Department, University of Alcalá, Campus Universitario, 28805 Alcalá de Henares, Spain
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in Sáez, A.; Bergasa, L.M.; Romeral, E.; López, E.; Barea, R.; Sanz, R. CNN-based Fisheye Image Real-Time Semantic Segmentation. In Proceedings of IEEE Intelligent Vehicles Symposium (IV), Changshu, China, 26–30 June 2018.
Sensors 2019, 19(3), 503;
Received: 12 December 2018 / Revised: 11 January 2019 / Accepted: 21 January 2019 / Published: 25 January 2019
(This article belongs to the Special Issue Deep Learning-Based Image Sensors)
The interest in fisheye cameras has recently risen in the autonomous vehicles field, as they are able to reduce the complexity of perception systems while improving the management of dangerous driving situations. However, the strong distortion inherent to these cameras makes the usage of conventional computer vision algorithms difficult and has prevented the development of these devices. This paper presents a methodology that provides real-time semantic segmentation on fisheye cameras leveraging only synthetic images. Furthermore, we propose some Convolutional Neural Networks(CNN) architectures based on Efficient Residual Factorized Network(ERFNet) that demonstrate notable skills handling distortion and a new training strategy that improves the segmentation on the image borders. Our proposals are compared to similar state-of-the-art works showing an outstanding performance and tested in an unknown real world scenario using a fisheye camera integrated in an open-source autonomous electric car, showing a high domain adaptation capability. View Full-Text
Keywords: fisheye; intelligent vehicle; CNN; deep learning; distortion fisheye; intelligent vehicle; CNN; deep learning; distortion
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MDPI and ACS Style

Sáez, Á.; Bergasa, L.M.; López-Guillén, E.; Romera, E.; Tradacete, M.; Gómez-Huélamo, C.; del Egido, J. Real-Time Semantic Segmentation for Fisheye Urban Driving Images Based on ERFNet. Sensors 2019, 19, 503.

AMA Style

Sáez Á, Bergasa LM, López-Guillén E, Romera E, Tradacete M, Gómez-Huélamo C, del Egido J. Real-Time Semantic Segmentation for Fisheye Urban Driving Images Based on ERFNet. Sensors. 2019; 19(3):503.

Chicago/Turabian Style

Sáez, Álvaro; Bergasa, Luis M.; López-Guillén, Elena; Romera, Eduardo; Tradacete, Miguel; Gómez-Huélamo, Carlos; del Egido, Javier. 2019. "Real-Time Semantic Segmentation for Fisheye Urban Driving Images Based on ERFNet" Sensors 19, no. 3: 503.

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