Abstract
Fast detection and accurate classification of traffic signs is one of the major aspects of advance driver assistance system (ADAS) and intelligent
transport systems (ITS), this paper presents a comparison between an 8-Layer convolutional neural network (CNN), and some state of the Arts model
such as VGG16 and Resnet50, for traffic sign classification on The GTSRB. using a GPU to increase processing time, the design showed that with various
augmentation applied to the CNN, our 8-layer Model was able to outperform the State of the Arts models with a higher test Accuracy, 50 times lesser
training parameters, and faster training time our 8 -layer model was able to achieve 96% test accuracy
transport systems (ITS), this paper presents a comparison between an 8-Layer convolutional neural network (CNN), and some state of the Arts model
such as VGG16 and Resnet50, for traffic sign classification on The GTSRB. using a GPU to increase processing time, the design showed that with various
augmentation applied to the CNN, our 8-layer Model was able to outperform the State of the Arts models with a higher test Accuracy, 50 times lesser
training parameters, and faster training time our 8 -layer model was able to achieve 96% test accuracy
| Original language | English |
|---|---|
| Pages (from-to) | 165-171 |
| Number of pages | 7 |
| Journal | International Journal of Scientific & Engineering Research (IJSER) |
| Volume | 12 |
| Issue number | 7 |
| Publication status | Published - 7 Jul 2021 |
| Externally published | Yes |