Traffic Sign Recognition based on Synthesised Training Data
2. Related Work
2.1. Early Methods
2.2. Deep Learning Methods
3. Generation of Synthetic Training Data
- Warning signs with their characteristics being their triangular shape, with red borders.
- Regulatory signs, which are usually found to have a circular shape, with varying colours (direction indicators or vehicles restrictions).
- Speed limits, which are circular with a red lining (for maximum speed), or blue (minimum speed).
3.2. Generating Novel Training Images from Templates
3.3. Dataset Normalisation
4. Classification Scheme
4.1. CNNs and Three-Dimensional Image Depth
4.2. Utilising Exponential Linear Units
4.4. Sample-Based Discretisation
- The size of the filter to be used. Most filters in max pooling are either of size 2 × 2 or 3 × 3, as based on these values, the kernel will traverse the entire image matrix. Furthermore, taking into account the use of the mean or max value at each traverse, the system will compute and output the suitable value.
- The stride of the kernel, as it defines the step that is used while passing through the image vector. A larger stride will resolve in a smaller output, since less pixels will overlap between kernel steps. For example, a 2 × 2 filter with a stride of two will resolve in non-overlapping pixels in the final output down-scaled feature vector.
5. Experimental Results
5.1. Implementation Details
5.2. Classification Results
6. Conclusions and Future Work
Conflicts of Interest
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Sample Availability: A sample of the code and results obtain can be found in: https://github.com/ alexandrosstergiou/Traffic-Sign-Recognition-basd-on-Synthesised-Training-Data.
|Spatial transform/inception ||99.98%|
|Human observations (best)||99.22%|
|Human observations (average)||98.84%|
|Multi-scale CNN ||98.31%|
|Random forests ||96.14%|
|LDA and HOG ||95.68%|
|Classifier Type||Accuracy Rates (%)||Kappa Statistic|
|CNN w/Leaky ReLU—0 epochs||87.88%||0.8788|
|CNN w/ PReLu, 50 epochs||87.03%||0.8703|
|CNN w/ ELU—50 epochs||87.88%||0.8788|
|CNN w/ ELU and BN, 50 epochs||92.20%||0.9219|
|CNN w/ ELU and BN, 100 epochs, new data||91.84%||0.9183|
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Stergiou, A.; Kalliatakis, G.; Chrysoulas, C. Traffic Sign Recognition based on Synthesised Training Data. Big Data Cogn. Comput. 2018, 2, 19. https://doi.org/10.3390/bdcc2030019
Stergiou A, Kalliatakis G, Chrysoulas C. Traffic Sign Recognition based on Synthesised Training Data. Big Data and Cognitive Computing. 2018; 2(3):19. https://doi.org/10.3390/bdcc2030019Chicago/Turabian Style
Stergiou, Alexandros, Grigorios Kalliatakis, and Christos Chrysoulas. 2018. "Traffic Sign Recognition based on Synthesised Training Data" Big Data and Cognitive Computing 2, no. 3: 19. https://doi.org/10.3390/bdcc2030019