Context-Aware and Attention-Driven Weighted Fusion Traffic Sign Detection Network
Guibao Wang, Kun Zhou, Lizhe Wang, Lanmei Wang
Abstract
The detection and recognition of traffic signs in complex environments has received extensive attention, and the correct detection of small targets and occluded targets are two key issues. This paper proposes a context-aware and attention-driven weighted fusion network for traffic sign detection. Specifically, the design of context module not only enhances the diversity of global features, but also reduces the sensitivity of convolution to occluded objects. In addition, the attention-driven weighted fusion feature pyramid is designed to efficiently fuse deep semantic information and shallow representation information, enhance the transfer of foreground positioning information, eliminate the problems of semantic spacing and noise interference in the process of feature fusion, and enhance the network’s representation of small and occluded traffic signs. The experimental results on the traffic sign dataset TT100K show that the method is effective and superior.