文献类型：期刊文献

英文题名：Improvement of park drivable area segmentation method based on STDCSeg network

作者：Zhang, Ting[1];Liu, Yuansheng[2];Fan, Yangyang[1];Lu, Ming[3]

第一作者：Zhang, Ting

通讯作者：Liu, YS[1]

机构：[1]Beijing Union Univ, Beijing Key Lab Informat Serv Engn, Beijing 100101, Peoples R China;[2]Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China;[3]Beijing Union Univ, Coll Appl Sci Technol, Beijing 100101, Peoples R China

第一机构：北京联合大学北京市信息服务工程重点实验室

通讯机构：[1]corresponding author), Beijing Union Univ, Coll Robot, Beijing 100101, Peoples R China.|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

年份：2025

卷号：7

期号：4

外文期刊名：DISCOVER APPLIED SCIENCES

收录：EI(收录号：20251618266318);Scopus(收录号：2-s2.0-105002786380);WOS:【ESCI(收录号:WOS:001459325200005)】；

基金：This research was funded by: (1) National Natural Science Foundation of China (62371013), (2) Beijing Municipal High-level Scientific Research Innovation Team Construction Support Program (BPHR20220121), (3) Supported by the Academic Research Projects of Beijing Union University (No. ZK20202404).

语种：英文

外文关键词：Semantic segmentation; Deep learning; Convolutional neural network; Park drivable area

摘要：With the increasing demand for smart mobile devices in park environments, there are higher requirements for their ability to adapt to complex scenarios. In unique park scenes such as lakeside paths and irregular stairs, the blurred road edges make it difficult for traditional algorithms to accurately distinguish between drivable and restricted areas, posing a significant challenge to the path planning and stable operation of smart mobile devices. To enhance the safe operation of these devices on complex paths, this study proposes an improved semantic segmentation method for park drivable areas, based on the Short-Term Dense Connection (STDC) network. This method effectively addresses issues such as imprecise road edge segmentation, insufficient road position awareness, and the limited generalization capability of existing datasets. Utilizing the STDC network as a backbone for key image feature extraction, a Multi-scale Coordinate Attention Module (MCAM) is introduced to bolster road position awareness and refine edge segmentation. Moreover, an Atrous Spatial Pyramid Pooling (ASPP) module is incorporated, allowing the network to manage local details and global contextual information within images. Besides, a dataset tailored to park environments is constructed using real-world and simulated park scene data for training and validation. The experimental results show that the algorithm achieved a mIoU of 98.91% on the self-built dataset, which is 3.07% higher than the STDC network. Additionally, its mIoU on the public dataset is 2.91% higher than the STDC network.