Rebalanced Region-Pixel Loss for Concrete Defects Segmentation
文献类型：会议论文
| 作者 | Hong KL(洪坤龙)2,3,4; Wang HG(王洪光)2,3 ; Yang L(杨亮)1; Yuan BB(袁兵兵)2,3
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| 出版日期 | 2021 |
| 会议日期 | September 17-19, 2021 |
| 会议地点 | Virtual, Online, China |
| 关键词 | Defects Semantic Segmentation Loss Function Concrete Surface Inspection Rebalanced training |
| 页码 | 137-143 |
| 英文摘要 | The deep convolution neural network (DCNN) using cross-entropy loss has already achieved high accuracy on the single defect classification task. However, for multi-defect segmentation, the imbalance of categories makes the segmentation of small targets (cracks) not meticulous enough. It also has to adjust the weight of categories manually. This research proposes a region-pixel loss function, which uses a rebalanced training method to balance category weights automatically and classify small categories more accurately. First, we use a wall-climbing robot to obtain the color and depth (RGB-D) information of the surface. Then, we adopt the visual based simultaneous localization and mapping (V-SLAM) method to select key-frame as the input of DCNN. Finally, reconstructing the 3D model of the environment with the output segmentation results. At the same time, to address the weak links of human-computer interaction in the post-processing of defect monitoring, a fast searching method from global 3D model to local details is proposed so that the staff can calmly obtain the local defects' detailed image. Experiments show that our loss function improves the intersection over union (IoU) of small flaws on the verification set. The designed global-to-local interaction method has been applied in the actual dam surface detection and provided an automated solution for monitoring large concrete surfaces. |
| 产权排序 | 1 |
| 会议录 | AIPR 2021 - 2021 4th International Conference on Artificial Intelligence and Pattern Recognition
 |
| 会议录出版者 | ACM |
| 会议录出版地 | New York |
| 语种 | 英语 |
| ISBN号 | 978-1-4503-8408-7 |
| 源URL | [http://ir.sia.cn/handle/173321/30578]  |
| 专题 | 工艺装备与智能机器人研究室 |
| 通讯作者 | Hong KL(洪坤龙) |
| 作者单位 | 1.The CCNY Robotics Lab Electrical Engineering Department, City College of New York New York, United States 2.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 3.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 4.University of Chinese Academy of Sciences, Beijing 100049, China |
| 推荐引用方式 GB/T 7714 | Hong KL,Wang HG,Yang L,et al. Rebalanced Region-Pixel Loss for Concrete Defects Segmentation[C]. 见:. Virtual, Online, China. September 17-19, 2021. |
入库方式： OAI收割
来源：沈阳自动化研究所
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