M-SKSNet: Multi-Scale Spatial Kernel Selection for Image Segmentation of Damaged Road Markings
文献类型：期刊论文
| 作者 | Wang, Junwei1,2; Liao, Xiaohan2; Wang, Yong2; Zeng, Xiangqiang1,2; Ren, Xiang2; Yue, Huanyin2; Qu, Wenqiu1,2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2024-05-01 |
| 卷号 | 16期号:9页码:1476 |
| 关键词 | remote sensing damaged road marking semantic segmentation deep learning |
| DOI | 10.3390/rs16091476 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | It is a challenging task to accurately segment damaged road markings from images, mainly due to their fragmented, dense, small-scale, and blurry nature. This study proposes a multi-scale spatial kernel selection net named M-SKSNet, a novel model that integrates a transformer and a multi-dilated large kernel convolutional neural network (MLKC) block to address these issues. Through integrating multiple scales of information, the model can extract high-quality and semantically rich features while generating damage-specific representations. This is achieved by leveraging both the local and global contexts, as well as self-attention mechanisms. The performance of M-SKSNet is evaluated both quantitatively and qualitatively, and the results show that M-SKSNet achieved the highest improvement in F1 by 3.77% and in IOU by 4.6%, when compared to existing models. Additionally, the effectiveness of M-SKSNet in accurately extracting damaged road markings from images in various complex scenarios (including city roads and highways) is demonstrated. Furthermore, M-SKSNet is found to outperform existing alternatives in terms of both robustness and accuracy. |
| WOS关键词 | EXTRACTION |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001220043300001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/205180]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Liao, Xiaohan |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Junwei,Liao, Xiaohan,Wang, Yong,et al. M-SKSNet: Multi-Scale Spatial Kernel Selection for Image Segmentation of Damaged Road Markings[J]. REMOTE SENSING,2024,16(9):1476. |
| APA | Wang, Junwei.,Liao, Xiaohan.,Wang, Yong.,Zeng, Xiangqiang.,Ren, Xiang.,...&Qu, Wenqiu.(2024).M-SKSNet: Multi-Scale Spatial Kernel Selection for Image Segmentation of Damaged Road Markings.REMOTE SENSING,16(9),1476. |
| MLA | Wang, Junwei,et al."M-SKSNet: Multi-Scale Spatial Kernel Selection for Image Segmentation of Damaged Road Markings".REMOTE SENSING 16.9(2024):1476. |
入库方式： OAI收割
来源：地理科学与资源研究所
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