Knowledge Commons of Institute of Automation,CAS
CADN: A weakly supervised learning-based category-aware object detection network for surface defect detection | |
Zou W(邹伟) | |
发表期刊 | Pattern Recognition |
ISSN | 0031-3203 |
2020 | |
卷号 | 109期号:0页码:10 |
通讯作者 | Su, Hu(hu.su@ia.ac.cn) |
摘要 | Large-scale data with human annotations is of crucial importance for training deep convolutional neu- ral network (DCNN) to ensure stable and reliable performance. However, accurate annotations, such as bounding box and pixel-level annotations, demand expensive labeling effort s, which has prevented wide application of DCNN in industries. Focusing on the problem of surface defect detection, this paper pro- poses a weakly supervised learning method named Category-Aware object Detection network (CADN) to tackle the dilemma. CADN is trained with image tag annotations only and performs image classification and defect localization simultaneously. The weakly supervised learning is achieved by extracting category- aware spatial information in a classification pipeline. CADN could be equipped with either a lighter or a larger backbone network as the feature extractor resulting in better real-time performance or higher ac- curacy. To address the two conflicting objectives simultaneously, both of which are significant concerns in industrial applications, knowledge distillation strategy is adopted to force the learned features of a lighter CADN to mimic that of a larger CADN. Accordingly, the accuracy of the lighter CADN is improved while high real-time performance is maintained. The proposed approach is verified on our own defect dataset as well as on an open-source defect dataset. As demonstrated, satisfied performance is achieved by the proposed method, which could meet industrial requirements completely. Meanwhile, the method mini- mizes human effort s involved in image labelling, thus promoting the applications of DCNN in industries. |
关键词 | Weakly supervised learning, Automated surface inspection, Defect detection, Knowledge distillation |
DOI | 10.1016/j.patcog.2020.107571 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2018YFB1306303] ; National Natural Science Foundation of China[61773374 and61702323] ; Major Basic Research Projects of Natural Science Foundation of Shandong Province[ZR2019ZD07] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Major Basic Research Projects of Natural Science Foundation of Shandong Province |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000573026300005 |
出版者 | ELSEVIER SCI LTD |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/40660 |
专题 | 中科院工业视觉智能装备工程实验室_精密感知与控制 |
通讯作者 | Zou W(邹伟) |
推荐引用方式 GB/T 7714 | Zou W. CADN: A weakly supervised learning-based category-aware object detection network for surface defect detection[J]. Pattern Recognition,2020,109(0):10. |
APA | Zou W.(2020).CADN: A weakly supervised learning-based category-aware object detection network for surface defect detection.Pattern Recognition,109(0),10. |
MLA | Zou W."CADN: A weakly supervised learning-based category-aware object detection network for surface defect detection".Pattern Recognition 109.0(2020):10. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
CADN.pdf(1839KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
个性服务 |
推荐该条目 |
保存到收藏夹 |
查看访问统计 |
导出为Endnote文件 |
谷歌学术 |
谷歌学术中相似的文章 |
[Zou W(邹伟)]的文章 |
百度学术 |
百度学术中相似的文章 |
[Zou W(邹伟)]的文章 |
必应学术 |
必应学术中相似的文章 |
[Zou W(邹伟)]的文章 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论