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Non-concentric Circular Texture Removal for Workpiece Defect Detection
Qin SJ(秦书嘉)1,2; Guo, Di1; Chen, Heping1; Xi N(席宁)3
Conference Name12th International Conference on Intelligent Robotics and Applications, ICIRA 2019
Conference DateAugust 8-11, 2019
Conference PlaceShenyang, China
Source PublicationIntelligent Robotics and Applications - 12th International Conference, ICIRA 2019, Proceedings
PublisherSpringer Verlag
Publication PlaceBerlin
Indexed ByEI
EI Accession number20193307310946
Contribution Rank1
KeywordDefect detection Non-concentric circle Small dataset
AbstractSince workpiece defect detection is a typical problem in computer vision with small datasets, generally its solutions cannot exploit the advantages of high accuracy, generalization ability, and neural network structures from the deep learning paradigm. Thus, traditional image processing techniques are still widely applied in such requirements. Aiming at three types of defects (crack, pitting and scratch) on a workpiece with non-concentric circular textures that severely interfere in the defect recognition stage, this paper proposes a sliding window filter for the texture detection. Experiments compare the proposed method with the polar coordinate mapping method and the T-smooth texture removal algorithm. Results show that the proposed method reveals the three types of defects better than the other two methods.
Document Type会议论文
Corresponding AuthorQin SJ(秦书嘉)
Affiliation1.Shenzhen Academy of Robotics, Shenzhen 518000, China
2.Shenyang Institute of Automation, CAS, Shenyang 110000, China
3.The University of Hong Kong, Pok Fu Lam, Hong Kong
Recommended Citation
GB/T 7714
Qin SJ,Guo, Di,Chen, Heping,et al. Non-concentric Circular Texture Removal for Workpiece Defect Detection[C]. Berlin:Springer Verlag,2019:576-584.
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