Lei Ma
ABSTRACT
Transportation is not only an importantly strategic hub of a country, but also an indispensable part of social life. With the development of science and technology, smart transportation has gradually been changed from a concept only to a reality. In consequence, automatic driving technology has become a hot research topic that attracts a number of attentions. Autonomous driving covers fully automated driving and assisted driving. And automatic driving technology based on deep learning is becoming more maturing and advanced. One of the necessary conditions for the development of automatic driving is to recognize traffic signals firstly. And the application of automatic driving technology in highway traffic has begun to show great achievements. However, research in the railway field is still relatively weak, and railway transportation plays a pivotal role in both national strategy and people's livelihood. Based on this gap and need, this paper proposes a lightweight to improve YOLOR object detection algorithm for railway traffic signal recognition method. The method uses YOLOv4-tiny as the baseline model, selectively adds implicit knowledge used by YOLOR, and improves the loss function to enhance the model's ability to prevent overfitting and solve long tail problem adaptively. In addition, the number and size of anchor frames in the self-made data set are optimized. Compared with the same lightweight YOLOv4-tiny model, AP has increased by 2.44, and the model file size only occupies 31.4M.
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