CASIA OpenIR  > 智能制造技术与系统研究中心  > 智能机器人
Learning to detect small impurities with superpixel proposals
Guo Y(郭跃)1,2; He YJ(贺一家)1,2; Song HT(宋海涛)2; Yuan K(原魁)2
Conference NameIEEE International Conference on Robotics and Biomimetics
Conference DateDecember 5-8, 2017
Conference PlaceMacau SAR, China
In this paper, we introduce a simplified end-to-end framework for impurity detection in opaque glass bottles with liquor that learns to directly distinguish between small impurities and backgrounds. Despite promising results using convolutional neural networks in various vision tasks, few works have provided specific solutions under inadequate exposures and large background fluctuations. Two contributions are made for this problem. Firstly, we have built a feasible detection system with a cascade hardware structure, and each FPGA provides a host computer with 12 images which are most confident for containing potential impurities respectively. Secondly, most previous convolutional network architectures generally work in large-scale notable object detection benchmarks, however, such networks cannot transfer well when detecting small objects in gray images. Therefore, we propose a superpixel proposal generation method for image augmentation and a fast convolutional network with an overlapped grid structure to detect small impurities, and experiments show that our binary detection results are comparable with human checkers.
KeywordImpurity Detection Superpixel Proposal Overlapped Grid Structure Convolutional Neural Network
Indexed ByEI
Document Type会议论文
Affiliation1.School of Computer and Control Engineering, University of Chinese Academy of Sciences
2.Institute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Guo Y,He YJ,Song HT,et al. Learning to detect small impurities with superpixel proposals[C],2017.
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