Research on Traffic Sign Detection Model Based on Transformer
[Objective]In order to solve the difficulties such as small target feature extraction,a transformer-based traffic sign detection model was proposed.[Method]Through fully utilizing the advantages of convolution and Transformer,a multi-scale feature extraction backbone model was established with attention fusion,which could enable the backbone network to selectively enhance the features of useful information and suppress the un-important ones with the support of global context information.In addition,pooling-like connection are incorpo-rated in order to prevent network degradation while enhancing feature fusion.Finally,experiments were conduct-ed on the TT100K dataset.[Result]The experimental results show that the meta-architecture with this model as the backbone achieves the highest mAP of 84%,and the maximum improvement of mAP is about 7%compared with the baseline model.[Conclusion]The model provides a new idea for traffic sign detection while improving feature extraction.
万方数据
维普
