Ruochen Dai, Mingfu Zhao, Bin Tang, Liyong Dai, Taojiang Wu, Shanghai Jiang. Wire Rope Defect-Detection Method Based on Otsu Segmentation and Edge Detection[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1630006
- Laser & Optoelectronics Progress
- Vol. 58, Issue 16, 1630006 (2021)
Fig. 1. Wire rope detection system
Fig. 2. Damaged wire rope sample
Fig. 3. Hyperspectral images of wire rope with defects. (a) 410 nm band image; (b) 470 nm band image; (c) 566 nm band image; (d) 661 nm band image; (e) 737 nm band image; (f) 793 nm band image
Fig. 4. Spectral curves of the wire rope area with the defect and the background area. (a) Reflectivity of randomly selected ROIs; (b) two parts of ROI reflectivity after average processing
Fig. 5. Hyperspectral images and gray histograms. (a) 406 nm band image and its grayscale histogram; (b) 566 nm band image and its grayscale histogram; (c) 645 nm band image and its grayscale histogram
Fig. 6. Image threshold segmentation results at different wavelengths. (a) 406 nm band image; (b) 566 nm band image; (c) 645 nm band image
Fig. 7. Comparison of edge detection results based on different operators. (a) Roberts operator; (b) Sobel operator; (c) Prewitt operator; (d) Canny operator
Fig. 8. Hyperspectral images after Hough transform. (a) Based on Roberts operator; (b) based on Sobel operator; (c) based on Prewitt operator; (d) based on Canny operator
|
Table 1. Number of pixels at different positions of the wire rope
|
Table 2. Treatment effect of different methods
Set citation alerts for the article
Please enter your email address
© Copyright 2018-2021 | Chinese Laser Press. All Rights Reserved 沪ICP备15018463号-20