所属单位：计算机科学与工程学院

发表刊物：2023 4th International Conference on Computer Vision, Image and Deep Learning

项目来源：自选课题

关键字：remote sensing; lightweight; CNN; semantic segmentation

摘要：Semantic segmentation, a fundamental task in computer vision, has developed rapidly in recent years. Semantic segmentation of remote sensing urban scene images, utilized in tasks such as land cover mapping, urban change detection, environmental preservation, and economic assessment, has also received much attention. In order to extract global semantic features, recent research has focused on combining transformers with CNN, which have great potential in global information modeling, for semantic segmentation models. However, the hybrid method of CNN and Vision Transformer still suffers from low latency. And the MLP of the visual transformer results in many parameters. To address these issues, we propose a lightweight pure CNN UNet(LPCUNet) model, which introduces a sizeable convolutional kernel to capture the global context. Moreover, a simple fusion module dynamically fuses local and global features. Extensive experiments show that our proposed method achieves state-of-the-art performance while faster latency. More precisely, the LPCUNet model demonstrated impressive performance with 83.3% and 86.4% mean Intersection over Union (mIoU) scores on the Vaihingen and Potsdam datasets respectively.

合写作者：武辉铸,鲍学良,仲昭昊

第一作者：刘冰

论文类型：论文集

页面范围：2

是否译文：否

发表时间：2023-05-14