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.
维普
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