An Adaptive Detection and Recognition Method for Traffic Sign Based on Multi-Scale Attention.

Traffic sign detection aims to locate and classify traffic signs in real time and accurately. But because of their small size and complex backgrounds, some smaller traffic signs are harder to detect than larger ones. On the other hand, some false information is always detected due to the influence of light changes and bad weather. Therefore, in order to solve the problems of missing detection and false detection, this paper proposes an adaptive multi-scale spatial-channel attention fusion center network (MSCA-Center Net). Firstly, a residual space-channel attention module combined with multi-channel information is proposed. The module divides the channels in the feature map into several groups, generate separate spatial and channel attention for each group, and combine the spatial and channel information of the multi-channel. Then, a multi-scale attention fusion module is proposed to integrate the extracted high-level and low-level features, which can improve the detection and classification accuracy. In addition, a coordinate attention module is introduced to obtain the final feature code, so that the model can locate and identify the target area more accurately. Finally, the optimal detection box is generated adaptively according to the feature coding. The proposed model was tested and evaluated on the CCTSDB dataset. Compared with the existing methods, the proposed method can detect traffic signs adaptively in real time under complex background, which verifies the effectiveness of the proposed method.