Joint Semantic Segmentation and Edge Detection for Waterline Extraction
文献类型:会议论文
作者 | Yuhang Chen3,4; Bolin Ni3,4; Gaofeng Meng2,3,4; Baoyin Sha1 |
出版日期 | 2022-05 |
会议日期 | 2021-11 |
会议地点 | 线上参会 |
英文摘要 | Automatic water gauge reading is very important for cargo weighting in ocean transportation. In this process, accurate waterline extraction is an important yet challenging step. Waterline extraction is subjected to many environmental interference factors, e.g., bad illumi- nation, bad weather conditions, ambiguous contours of water stain, etc. In this paper, we propose a joint multitask based deep model for ac- curate waterline extraction. The proposed model consists of two main branches. One branch is used to extract high-level contextual informa- tion. The other branch rooted in shallow layers is used to extract low-level detail features. The two branches are later coupled with each other to co- supervise the estimation of waterline. Our model works well on various conditions, such as uneven light, serious reections, etc. We also intro- duce a new benchmark dataset for waterline extraction. This dataset consists of 360 pictures extracted from 69 videos collected in several ac- tual ports. Furthermore, su cient experiments show that our model is e ective on the introduced dataset and outperforms the state-of-the-art methods. |
语种 | 英语 |
源URL | [http://ir.ia.ac.cn/handle/173211/48748] |
专题 | 自动化研究所_模式识别国家重点实验室_遥感图像处理团队 |
通讯作者 | Gaofeng Meng |
作者单位 | 1.Coal Science and Technology Research Institute 2.Centre for Artificial Intelligence and Robotics, HK Institute of Science Innovation, Chinese Academy of Sciences 3.School of Artificial Intelligence, University of Chinese Academy of Sciences 4.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Yuhang Chen,Bolin Ni,Gaofeng Meng,et al. Joint Semantic Segmentation and Edge Detection for Waterline Extraction[C]. 见:. 线上参会. 2021-11. |
入库方式: OAI收割
来源:自动化研究所
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