Semantic Segmentation Network for Unstructured Rural Roads Based on Improved SPPM and Fused Multiscale Features
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Advanced Neural Network Applications · Automated Road and Building Extraction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Research Center for Intelligent Agriculture, Ministry of Education Engineering, Urumqi 830052, China
Xinjiang Agricultural Informatization Engineering, Technology Research Center, Urumqi 830052, China
National Engineering Research Center for Information Technology in Agriculture, Beijing 100125, China
Funding
National Key R&D Program of China · Provincial Key S&T Program of Xinjiang · Research on key technology of rural road image panoramic segmentation for the complex environment
References (26)
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