Real-Time Traffic Sign Detection for Self-Driving and Energy-Saving Driving Based on YOLOv4 Neural Network
Chi‐Chun Chen, Yuan-Hong Guan, Nabila Rizqia Novianda, Chung-Chen Teng, Meng-Hua Yen · 2022
With the booming development of autonomous vehicles (AV) in recent years, a vehicle needs to have the ability to detect changes in the environment in real-time. If the vehicle can be decelerated in advance according to the traffic signs, it can effectively reduce fuel consumption and improve overall comfort. This paper uses the Kaggle data set for training based on marking the common traffic signs in foreign countries, adds the local data set in Taiwan to the testing data set, and uses the You Only Look Once v4 (YOLOv4) neural network to detect the traffic signs in real time. The experimental results show that YOLOv4 still has a good generalization ability in the case of slight differences in different national sign types, and the mean Average Precision (mAP) can reach more than 87.6%.