CR2-Net: Component Relationship Reasoning Network for Traffic Text Detection

Traffic text, as a special type of natural scene text, plays an important role in intelligent transportation. Apart from the challenges faced by general natural scene text detection, the presence of a large amount of non-traffic text which leads to false alarms further complicates the problem of traffic text detection, e.g., billboards, building signs, etc. In this work, we infer text component relationships via a component relationship reasoning network (CR2-Net) for traffic text detection. The method contains main contributions as follows: (1) Explored a GCN-based component-relational reasoning traffic text detection network by leveraging global contextual information and reasoning about intrinsic connections between text components to improve traffic text detection performance. (2) Designed a Group Deformable Convolution Network (GDCN) and adaptively adjusting the convolutional manner to enhance the feature extraction capability of the network for text regions. (3) Proposed a Fusion Attention Module (FAM) to enhance the network adaptive ability by assigning feature map weights to different channels. To demonstrate the effectiveness, we evaluate CR2-Net on four standard benchmarks. Experimental results demonstrate that our method achieves remarkable performance on two traffic text datasets, i.e., CTST-1600, and TPD. Especially, our results on TPD dataset surpass all previously reported results to the best of our knowledge. Additionally, our approach exhibits competitive results on general natural scene text datasets, such as ICDAR 2015, and SCUT-CTW1500, thereby fully demonstrating the superiority and adaptability of our method.