Unstructured Road Extraction in UAV Images based on Lightweight Model

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外文摘要：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 theTMBNet network has advantages in terms of unstructured road extraction.

外文关键词：

Unstructured roadLightweight modelTriple Multi-Block(TMB)Semantic segmentation network

作者：

Di Zhang、Qichao An、Xiaoxue Feng、Ronghua Liu、Jun Han、Feng Pan

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作者单位：

School of Automation,Beijing Institute of Technology,Beijing 100081,China

Tianjin Institute of Maritime Navigation Instruments,Tianjin 300130,China

The 716th Research Institute of China Shipbuilding Group Co.,Ltd,Jiangsu 222061,China

基金：

国家自然科学基金国家自然科学基金国家自然科学基金Technical Field Foundation of the National Defense Science and Technology 173 Program of ChinaTechnical Field Foundation of the National Defense Science and Technology 173 Program of China

项目编号：

6226116057561991414619730362022060105320220601030

出版年：

2024

DOI：

10.1186/s10033-024-01018-4

中国机械工程学报

中国机械工程学会

中国机械工程学报

CSTPCD

影响因子：0.765

ISSN：1000-9345

年,卷(期)：2024.37(2)