Knowledge Commons of Institute of Automation,CAS
Beyond Crack: Fine-Grained Pavement Defect Segmentation Using Three-Stream Neural Networks | |
Zhang, Yujia1; Wu, Junxian1,2; Li, Qianzhong1,2; Zhao, Xiaoguang1; Tan, Min1 | |
发表期刊 | IEEE Transactions on Intelligent Transportation Systems |
ISSN | 1524-9050 |
2021-12-22 | |
卷号 | /期号:/页码:/ |
通讯作者 | Zhang, Yujia(zhangyujia2014@ia.ac.cn) |
摘要 | Pavement defect segmentation is a fundamental task in the field of transport infrastructure inspection. Existing methods mainly focus on detection/segmentation for long and thin cracks. However, there are many other types of defects with various sizes and shapes that are also essential to segment, which brings more challenges toward detailed road inspection. To address the above problems and provide a more comprehensive understanding of the overall road conditions, we propose a three-stream neural network that combines spatial, contextual and boundary information for fine-grained defect segmentation. Specifically, the spatial stream captures rich low-level spatial features. The contextual stream utilizes an attention mechanism and models high-level contextual relationships over local features. To further refine the segmentation results, the boundary stream encodes detailed boundaries using a global gated convolution and generates additional boundary maps. By combining the above different information, our model can effectively produce pixel-wise predictions for fine-grained road inspection. The network is trained using a dual-task loss in an end-to-end manner, and experiments were performed on three newly collected datasets, i.e., a fine-grained defect dataset and two crack datasets, which shows that the proposed method achieves favorable segmentation results on complex multi-class defects, and is also able to segment single-class cracks. Specifically, on the fine-grained dataset, it achieved state-of-the-art performance over other competing baselines (mPA of 0.54, mIoU of 0.38, Mic$\_$F of 0.78 and Mac$\_$F of 0.65), where each image is resized to 512 $\times$ 512 and the processing speed is 21 FPS on average. |
关键词 | Fine-grained defect segmentation Crack detection Semantic segmentation Pavement inspection |
DOI | 10.1109/TITS.2021.3134374 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Project of China[2019YFB1310601] ; National Key Research and Development Program of China[2017YFC0820203] ; National Natural Science Foundation of China[62103410] |
项目资助者 | National Key Research and Development Project of China ; National Key Research and Development Program of China ; National Natural Science Foundation of China |
WOS研究方向 | Engineering ; Transportation |
WOS类目 | Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology |
WOS记录号 | WOS:000858988900062 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 多模态智能 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/47421 |
专题 | 复杂系统认知与决策实验室_先进机器人 |
通讯作者 | Zhang, Yujia |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhang, Yujia,Wu, Junxian,Li, Qianzhong,et al. Beyond Crack: Fine-Grained Pavement Defect Segmentation Using Three-Stream Neural Networks[J]. IEEE Transactions on Intelligent Transportation Systems,2021,/(/):/. |
APA | Zhang, Yujia,Wu, Junxian,Li, Qianzhong,Zhao, Xiaoguang,&Tan, Min.(2021).Beyond Crack: Fine-Grained Pavement Defect Segmentation Using Three-Stream Neural Networks.IEEE Transactions on Intelligent Transportation Systems,/(/),/. |
MLA | Zhang, Yujia,et al."Beyond Crack: Fine-Grained Pavement Defect Segmentation Using Three-Stream Neural Networks".IEEE Transactions on Intelligent Transportation Systems /./(2021):/. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Beyond_Crack_Fine-Gr(12585KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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