Unstructured Road Extraction in UAV Images based on Lightweight Model

Abstract There is no unified planning standard for unstructured roads, and the morphological structures of these roads are complex and varied. It is important to maintain a balance between accuracy and speed for unstructured road extraction models. Unstructured road extraction algorithms based on deep learning have problems such as high model complexity, high computational cost, and the inability to adapt to current edge computing devices. Therefore, it is best to use lightweight network models. Considering the need for lightweight models and the characteristics of unstructured roads with different pattern shapes, such as blocks and strips, a TMB (Triple Multi-Block) feature extraction module is proposed, and the overall structure of the TMBNet network is described. The TMB module was compared with SS-nbt, Non-bottleneck-1D, and other modules via experiments. The feasibility and effectiveness of the TMB module design were proven through experiments and visualizations. The comparison experiment, using multiple convolution kernel categories, proved that the TMB module can improve the segmentation accuracy of the network. The comparison with different semantic segmentation networks demonstrates that the TMBNet network has advantages in terms of unstructured road extraction.