文献类型： 外文期刊

作者： Cao, Xinyu ¹ ; Tian, Yongqiang ¹ ; Yao, Zhixin ¹ ; Zhao, Yunjie ¹ ; Zhang, Taihong ¹ ;

作者机构： 1.Xinjiang Agr Univ, Sch Comp & Informat Engn, Urumqi 830052, Peoples R China

2.Minist Educ Engn, Res Ctr Intelligent Agr, Urumqi 830052, Peoples R China

3.Technol Res Ctr, Xinjiang Agr Informatizat Engn, Urumqi 830052, Peoples R China

4.Natl Engn Res Ctr Informat Technol Agr, Beijing 100125, Peoples R China

关键词： semantic segmentation; rural roads; bottleneck attention; strip pooling; feature fusion

期刊名称：APPLIED SCIENCES-BASEL （ 影响因子：2.5； 五年影响因子：2.7 ）

ISSN：

年卷期： 2024 年 14 卷 19 期

页码：

收录情况： SCI

摘要： Semantic segmentation of rural roads presents unique challenges due to the unstructured nature of these environments, including irregular road boundaries, mixed surfaces, and diverse obstacles. In this study, we propose an enhanced PP-LiteSeg model specifically designed for rural road segmentation, incorporating a novel Strip Pooling Simple Pyramid Module (SP-SPPM) and a Bottleneck Unified Attention Fusion Module (B-UAFM). These modules improve the model's ability to capture both global and local features, addressing the complexity of rural roads. To validate the effectiveness of our model, we constructed the Rural Roads Dataset (RRD), which includes a diverse set of rural scenes from different regions and environmental conditions. Experimental results demonstrate that our model significantly outperforms baseline models such as UNet, BiSeNetv1, and BiSeNetv2, achieving higher accuracy in terms of mean intersection over union (MIoU), Kappa coefficient, and Dice coefficient. Our approach enhances segmentation performance in complex rural road environments, providing practical applications for autonomous navigation, infrastructure maintenance, and smart agriculture.