Traffic Sign Instances Segmentation Using Aliased Residual Structure and Adaptive Focus Localizer
Wenjun Shi, Yingjun Shi, Dongchen Zhu, Xiaolin Zhang, Jiamao Li · 2022 26th International Conference on Pattern Recognition (ICPR) · 2022
Traffic sign recognition plays a crucial role in both unmanned vehicles and advanced driver assistance systems. Although many recent deep-learning-based approaches have made some progress on this task, it still suffers from the significant changes in scale and rotation, the presence of objects of the same color appearance, as well as the high similarity between classes. In this paper, we first propose a traffic sign detection network, named AAMNet, by adopting the generally framework of one-stage anchor-free instance segmentation models. For the feature extraction, a novel Aliased residual block is designed to support the encoder to retain more detailed information from the shallow low-level features. For decoder, we introduce a channel attention module into the mask generator to implement an Adaptive focus localizer head, which can filter out the irrelevant prototype masks. A Mask guided center loss is further constructed to improve the localization accuracy. Then, considering the difficulty of text based traffic signs recognition, for the first time, we developed a text-traffic-signs dataset TextTSD covering rich scenes and multiple languages. Extensive experiments both on TT100k and TextTSD show that our AAMNet gains a competitive performance compared with several state-of-the-art methods.