Name
Title | ||
|---|---|---|
Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods. |
Abstract | ||
|---|---|---|
This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neunet.2018.01.005 | Neural Networks |
Keywords | Field | DocType |
Deep learning,Traffic sign,Spatial transformer network,Convolutional neural network | Stochastic gradient descent,Convolutional neural network,Stochastic process,Traffic sign recognition,Artificial intelligence,Deep learning,Traffic sign,Artificial neural network,Machine learning,Mathematics,Benchmarking | Journal |
Volume | Issue | ISSN |
99 | 1 | 0893-6080 |
Citations | PageRank | References |
14 | 0.73 | 19 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Álvaro Arcos García | 1 | 25 | 1.39 |
| Juan Antonio Álvarez | 2 | 26 | 5.29 |
| Luis Miguel Soria-Morillo | 3 | 47 | 5.99 |