Traffic Sign Recognition With Lightweight Two-Stage Model in Complex Scenes.

Traffic sign recognition with high accuracy and real-time is an important part of the intelligent transportation system. In this article, based on large-scale traffic signs and the inherent conflict between location regression and classification of traffic signs, we propose a novel and flexible two-stage approach. It combines a lightweight superclass detector with a refinement classifier. The main contributions lie in three aspects: (1) We use locations and sizes of signs as prior knowledge to establish a probability distribution model. It can significantly decrease the search range of signs and improve the processing speed, as well as reducing false detection. (2) We propose a high-performance lightweight superclass detector. We introduce the Inception and Channel Attention, by generating multi-scale receptive fields and adaptively adjusting channel features. It alleviates the large scale variance challenge of objects and the interference of background information. Meanwhile, we present a merging Batch Normalization and multi-scale testing method to further improve detection performance. (3) We propose a refinement classifier based on similarity measure learning for the subclass classification. It increases the precision of discriminating similar subclasses and also improves the extensibility of our approach. Our two-stage approach is simple and effective, whose paradigm is different from others. Experiments on the Tsinghua-Tencent 100K dataset demonstrate the performance of our approach. Compared with the state-of-the-art methods, our method achieves competitive performance (92.16% mAP) with a lightweight detector ($6.49M $). The processing time is $0.150s $ per frame, of which the speed is increased by 3 times compared with existing methods. [ABSTRACT FROM AUTHOR]