Traffic Scene Semantic Segmentation Algorithm with Knowledge Distillation of Multi-level Features Guided by Boundary Perception
To solve the problems of object detail information loss and large model parameters in traffic scenes,a traffic scene semantic segmentation algorithm with knowledge distillation of multi-level features guided by boundary perception is proposed.The proposed algorithm can smooth the object segmentation boundaries with fewer parameters.First,the adaptive fusing multi-level feature module is constructed to integrate the multi-level features of deep semantic information and shallow spatial information.The object boundary information and object subject information are highlighted selectively.Second,an interactive attention fusion module is proposed to model the long-range dependencies in spatial and channel dimensions,enhancing the information interaction capabilities between different dimensions.Finally,a boundary loss function based on candidate boundaries is proposed to construct a boundary knowledge distillation network based on detail awareness and transfer boundary information from complex teacher networks.Experiments on the traffic scene datasets Cityscapes and CamVid demonstrate that the proposed algorithm achieves a lightweight model while gaining positive segmentation performance,maintaining significant advantages in dealing with small and slender objects.
万方数据
维普
