An Efficient Framework for Detection and Recognition of Numerical Traffic Signs

Due to the variety of categories and uneven distribution of available samples, automatic traffic sign detection and recognition is still a challenging task. For those categories with less training data, existing deep learning methods cannot achieve desirable performance, and the overall detection effect is not satisfactory as well. In this letter, we fully explore the relationship between different traffic signs with digital characters and transform the category objects into multi-level classes to alleviate the uneven distribution of samples. We design a lightweight two-stage object detection framework with high real-time performance. The first stage network is proposed to obtain the category groups of traffic signs, and then we construct another object detection network to identify the digital characters of the detected traffic signs. To make the prediction in the first stage more accurate, we put forward a boxes fusion algorithm in the post-processing process and a refine module to improve the recognition performance. Experimental results show that our approach possesses significantly improved performance compared with the latest object detection networks and other traffic sign detectors. Even some traffic signs that only exist in testset can also be recognized accurately by our method.