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Switching network for mixing experts with application to traffic sign recognition

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Bibliographic Details
Main Authors: Amir, Ahangi, Rico, Möckel
Format: Book
Language:English
Published: Springer 2023
Subjects:
CNNs
Traffic Sign Recognition Benchmark dataset
Online Access:https://link.springer.com/article/10.1007/s11042-023-14959-0
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8345
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spelling oai:localhost:PNK-83452023-04-27T01:52:22Z Switching network for mixing experts with application to traffic sign recognition Amir, Ahangi Rico, Möckel CNNs Traffic Sign Recognition Benchmark dataset CC BY The correct and robust recognition of traffic signs is indispensable to self-driving vehicles and driver-assistant systems. In this work, we propose and evaluate two network architectures for multi-expert decision systems that we test on a challenging Traffic Sign Recognition Benchmark dataset. The decision systems implement individual experts in the form of deep convolutional neural networks (CNNs). A gating network CNN acts as final decision unit and learns which individual expert CNNs are likely to contribute to an overall meaningful classification of a traffic sign. The gating network then selects the outputs of those individual expert CNNs to be fused to form the final decision. 2023-04-27T01:52:22Z 2023-04-27T01:52:22Z 2023 Book https://link.springer.com/article/10.1007/s11042-023-14959-0 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8345 en application/pdf Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic CNNs
Traffic Sign Recognition Benchmark dataset
spellingShingle CNNs
Traffic Sign Recognition Benchmark dataset
Amir, Ahangi
Rico, Möckel
Switching network for mixing experts with application to traffic sign recognition
description CC BY
format Book
author Amir, Ahangi
Rico, Möckel
author_facet Amir, Ahangi
Rico, Möckel
author_sort Amir, Ahangi
title Switching network for mixing experts with application to traffic sign recognition
title_short Switching network for mixing experts with application to traffic sign recognition
title_full Switching network for mixing experts with application to traffic sign recognition
title_fullStr Switching network for mixing experts with application to traffic sign recognition
title_full_unstemmed Switching network for mixing experts with application to traffic sign recognition
title_sort switching network for mixing experts with application to traffic sign recognition
publisher Springer
publishDate 2023
url https://link.springer.com/article/10.1007/s11042-023-14959-0
https://dlib.phenikaa-uni.edu.vn/handle/PNK/8345
_version_ 1764358630267682816
score 8.881002

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