| Autor: |
Kousalya, K., Mohana, R. S., Kavitha, E., Kavya, T., Keerthana, S. |
| Předmět: |
|
| Zdroj: |
AIP Conference Proceedings; 2021, Vol. 2387/2429 Issue 1, p1-6, 6p |
| Abstrakt: |
Autonomous cars must take real-time decisions about surroundings to reduce death rates during traffic accidents. Traffic related information's are available in the Traffic sign. It assists to drive better and safer. In the traditional method, the traffic sign is detected by manually or computer vision methods. Those are time consuming processes.No-one will be aware of all traffic signs, so it will let everyone know and learn the signs easily. Traffic sign recognition is just one of the problems that computer vision and deep learning can solve Convolution Neural Network (CNN) architecture.Machinesareable-to identify traffic signs from the German Traffic Signal Recognition Benchmark (GTSRB) dataset that contains forty-three classes. The proposed system has three working stages: image pre-processing, detection, and recognition. Initially, the traffic sign image is pre-processed, and the detailed information present in the traffic sign image is detected by using the histogram equalization method, which improves the contrast of the traffic sign image. After preprocessing, the features of the images are extracted by using CNN architecture with three non-linear activation functions such as Re-Lu, Leaky-Re-Lu and sigmoid. The experimental results compare the results of the above three non-linear activation functions.The activation function Re-Lu and Leaky Re-Lu achieved accuracy above 95%.After feature extraction, the output layer is used to predict the traffic sign images. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
