Real-time segmentation algorithm of unstructured road scenes based on improved BiSeNet

In response to the fuzzy and complex boundaries of unstructured road scenes, as well as the high difficulty of segmentation, this paper uses BiSeNet as the benchmark model to improve the above situation and proposes a real-time segmentation model based on partial convolution. Using FasterNet based on partial convolution as the backbone network and improving it, adopting higher floating-point operations per second operators to improve the inference speed of the model; optimizing the model structure, removing inefficient spatial paths, and using shallow features of context paths to replace their roles, reducing model complexity; the Residual Atrous Spatial Pyramid Pooling Module is proposed to replace a single context embedding module in the original model, allowing better extraction of multi-scale context information and improving the accuracy of model segmentation; the feature fusion module is upgraded, the proposed Dual Attention Features Fusion Module is more helpful for the model to better understand image context through cross-level feature fusion. This paper proposes a model with a inference speed of 78.81 f/s, which meets the real-time requirements of unstructured road scene segmentation. Regarding accuracy metrics, the model in this paper excels with Mean Intersection over Union and Macro F1 at 72.63% and 83.20%, respectively, showing significant advantages over other advanced real-time segmentation models. Therefore, the real-time segmentation model based on partial convolution in this paper well meets the accuracy and speed required for segmentation tasks in complex and variable unstructured road scenes, and has reference value for the development of autonomous driving technology in unstructured road scenes. Code is available at https://github.com/BaiChunhui2001/Real-time-segmentation.