Image Denoising of Seam Images With Deep Learning for Laser Vision Seam Tracking
文献类型：期刊论文
| 作者 | Yang, Lei2,3 ; Fan, Junfeng1; Huo, Benyan2,3; Li, En1; Liu, Yanhong2,3
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| 刊名 | IEEE SENSORS JOURNAL
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| 出版日期 | 2022-03-15 |
| 卷号 | 22期号:6页码:6098-6107 |
| ISSN号 | 1530-437X |
| 关键词 | Welding Laser noise Sensors Robots Laser modes Feature extraction Lasers Deep network architecture image denoising seam tracking robot welding structured light vision |
| DOI | 10.1109/JSEN.2022.3147489 |
| 通讯作者 | Liu, Yanhong(liuyh@zzu.edu.cn) |
| 英文摘要 | Seam tracking with structured light vision has been widely applied into the robot welding. And the precise laser stripe extraction is the premise of automatic laser vision seam tracking. However, conventional laser stripe extraction methods based on image processing have the shortcomings of poor flexibility and robustness, which are easily affected by considerable image noises in the welding processing, such as arc light, smoke, and splash. To address this issue, inspired by image segmentation, with the strong contextual feature expression ability of deep convolutional neural network (DCNN), a novel image denoising method of seam images is proposed in this paper for automatic laser stripe extraction to serve intelligent robot welding applications, such as seam tracking, seam type detection, weld bead detection, etc. With the deep encoder-decoder network framework, aimed at the information loss issue by multiple convolutional and pooling operations in DCNNs, an attention dense convolutional block is proposed to extract and accumulate multi-scale feature maps. Meanwhile, a residual bi-directional ConvLSTM block (BiConvLSTM) is proposed to effectively learn multi-scale and long-range spatial contexts from local feature maps. Finally, a weighted loss function is proposed for model training to address the class unbalanced issue. Combined with the seam image set, the experimental results show that the proposed image denoising network could correctly extract the laser stripes from seam images which could demonstrate that the proposed method shows a high detection precision and good robustness against the strong image noise interference from welding process. |
| WOS关键词 | EXTRACTION ; SYSTEM ; IDENTIFICATION ; JOINT |
| 资助项目 | National Natural Science Foundation of China[62003309] ; National Key Research and Development Project of China[2020YFB1313701] ; Science and Technology Research Project, Henan Province, China[202102210098] ; Outstanding Foreign Scientist Support Project in Henan Province of China[GZS2019008] |
| WOS研究方向 | Engineering ; Instruments & Instrumentation ; Physics |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000770054800134 |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Project of China ; Science and Technology Research Project, Henan Province, China ; Outstanding Foreign Scientist Support Project in Henan Province of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/48157]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 复杂系统管理与控制国家重点实验室_水下机器人 |
| 通讯作者 | Liu, Yanhong |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Robot Percept & Control Engn Lab, Zhengzhou 450001, Henan, Peoples R China 3.Zhengzhou Univ, Sch Elect Engn, Zhengzhou 450001, Henan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Lei,Fan, Junfeng,Huo, Benyan,et al. Image Denoising of Seam Images With Deep Learning for Laser Vision Seam Tracking[J]. IEEE SENSORS JOURNAL,2022,22(6):6098-6107. |
| APA | Yang, Lei,Fan, Junfeng,Huo, Benyan,Li, En,&Liu, Yanhong.(2022).Image Denoising of Seam Images With Deep Learning for Laser Vision Seam Tracking.IEEE SENSORS JOURNAL,22(6),6098-6107. |
| MLA | Yang, Lei,et al."Image Denoising of Seam Images With Deep Learning for Laser Vision Seam Tracking".IEEE SENSORS JOURNAL 22.6(2022):6098-6107. |
入库方式： OAI收割
来源：自动化研究所
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