http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Zebin Su (검색결과 1 건)
- 무료
- 기관 내 무료
- 유료
-
Detection of Bobbin Yarn Surface Defects by Visual Saliency Analysis
Junfeng Jing,Haiye Li,Huanhuan Zhang,Zebin Su,Kaibing Zhang 한국섬유공학회 2020 Fibers and polymers Vol.21 No.11
In order to solve the problem of unstable quality of the traditional bobbin yarn surface defect detection, a methodfor detecting the surface defect of bobbin yarn using visual saliency analysis is proposed. Firstly, the Difference of Gaussianalgorithm is used to suppress the texture of the image, and the contrast between the defect area and the background isenhanced. Then, the method of wavelet threshold is applied to filter the noise in the images after the Difference of Gaussian,and the detailed features of the defect area are preserved while the noise interference is eliminated. Finally, the frequencytuned visual saliency algorithm is used to extract and segment the defective regions accurately. After the process about theDifference of Gaussian and wavelet threshold denoising, the original image of the bobbin yarn can make the image highlightthe feature information of the defect area on the basis of ensuring elimination of the background texture and noise, andfacilitate the detection of the defect area by combining visual saliency analysis. In the detection of the accuracy and integrityof the defect area, the detection method of this paper is better than the control method, and the defect segmentation result ismore accurate. The proposed method for detecting the surface defect of the bobbin can not only completely eliminate theinfluence of the background texture of the bobbin surface, but also accurately and completely detect the surface defect of thebobbin, and the defect details remain intact. In this paper, the effectiveness of the algorithm is further verified by objectivetests. The precision and recall rate of the test are 95.15 % and 98.00 %, respectively, which can meet the needs of actualdetection.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
