A Driving Warning Method based on YOLOV3 and Neural Network
Xinxin He, Zhi Zheng · 2019
Unmanned driving warning is one of the important core issues of unmanned driving decision. This paper studies the method of prediction on the traffic warning information for unmanned driving warning, which is based on YOLOV3 and BP neural network. Firstly, weight training is carried out through self-made data set. Secondly, the trained YOLOV3 model is used to classify the image and video data: car, bicycle, bus, motorbike, person. Finally, the YOLOV3 target detection information will be classified into 5 categories by the BP neural network: safe, stop, slow, left, right. The result of the simulated experiment shows that the model works well. Based on different data set, the accuracy of the second classification is up to 0.99.