Switching network for mixing experts with application to traffic sign recognition
CC BY
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| Định dạng: | Sách |
| Ngôn ngữ: | English |
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Springer
2023
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| Truy cập trực tuyến: | https://link.springer.com/article/10.1007/s11042-023-14959-0 https://dlib.phenikaa-uni.edu.vn/handle/PNK/8345 |
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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 |