Regularized CNN for Traffic Sign Recognition
Author:
Affiliation:
1. Maulana Azad National Institute of Technology,Dept. of Computer Science Engineering,Bhopal,India
2. Maulana Azad National Institute of Technology,Dept. of Computer Science Department,Bhopal,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9761170/9761283/09761341.pdf?arnumber=9761341
Reference9 articles.
1. BBAS: Towards large scale effective ensemble adversarial attacks against deep neural network learning
2. DeepThin: A novel lightweight CNN architecture for traffic sign recognition without GPU requirements
3. Few-shot traffic sign recognition with clustering inductive bias and random neural network
4. Do not get fooled: Defense against the one-pixel attack to protect IoT-enabled Deep Learning systems
5. Multi-layer adversarial domain adaptation with feature joint distribution constraint
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1. Persian Traffic Sign Classification Using Convolutional Neural Network and Transfer Learning;Arabian Journal for Science and Engineering;2024-04-16
2. Hybrid Image Improving and CNN (HIICNN) Stacking Ensemble Method for Traffic Sign Recognition;IEEE Access;2023
3. Customized CNN for Traffic Sign Recognition Using Keras Pre-Trained Models;International Conference on Innovative Computing and Communications;2023
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