文献类型：期刊文献

英文题名：Semantic Segmentation Method for Drivable Areas under Unstructured Road

作者：Zhang, Ting[1]; Liu, Yuansheng[2]

第一作者：Zhang, Ting

机构：[1] Beijing Union University, Beijing Key Laboratory of Information Service Engineering, Beijing, 100101, China; [2] Beijing Union University, College of Robotics, Beijing, 100101, 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：[2]Beijing Union University, College of Robotics, Beijing, 100101, China|[1141739]北京联合大学机器人学院;[11417]北京联合大学;

年份：2024

外文期刊名：2024 2nd International Conference on Computer, Vision and Intelligent Technology, ICCVIT 2024 - Proceedings

收录：EI(收录号：20251018004368)

语种：英文

外文关键词：Convolutional neural networks - Deep neural networks - Deep reinforcement learning - Network coding - Parking

摘要：To improve the ability of autonomous vehicles to recognize drivable areas on park roads, this study puts foward a semantic segmentation approach based on the STDC (Short-Term Dense Connection) network. On this foundation of the STDC network, a deep convolutional neural network with an encoder-decoder structure is designed, and a double-chain cascaded feature fusion module (DCFFM) is introduced. Additionally, the Convolutional Block Attention Module (CBAM) is incorporated to dynamically adjust channel and spatial attention, thereby reinforcing the model s capacity to discern crucial features. A dataset suitable for drivable area segmentation in park environments was collected and used for training and validation. The experimental outcomes demonstrate that the algorithm attained a mIoU of 98.41% on the self-built dataset, representing a 2.73 percentage point improvement over the STDC network. Furthermore, the mIoU on public datasets exhibited a 2.55 percentage point enhancement in comparison to that of the STDC network. ? 2024 IEEE.