Load Position Detection of Container Crane Using Camera
Main Article Content
Abstract
In this study, the authors proposed an image processing algorithm to detect (measure) the rope length of container crane (distance from camera system to container spreader) and sway angle of the spreader (container). This measurement will be the main input to design the anti-sway control system for container cranes. The image processing algorithm includes the main steps: converting from BGR color space to HSV color space, then, binary image is used to extract the marker area. Next, the Canny boundary detection technique is applied to determine the boundary of the markers in the container spreader. The center location of each marker is determined and used to calculate the distance from the camera system to the container spreader. The rope length accuracy by the image processing algorithm is 99.79%, which is sufficient for crane control purposes.
Keywords
image processing, rope length detection, container crane, marker, sway angle
Article Details
References
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[3] Anh Viet Nguyen, Young-Yeol Cho, Eung-Joo Lee. Container Dimension Detection and 3D Modeling based on Stereo Vision. MIT, 2008, pp. 207-210.
[4] Wang, Shikai. Color Image Segmentation based on Color Similarity. International Conference on Computational Intelligence and Software Engineering, 2009, pp. 1-4.
[5] Tse-Wei Chen, Yi-Ling Chen, Shao-Yi Chien, Fast Image Segmentation based on K-Means Clustering with Histograms in HSV Color Space, IEEE 10th Workshop on Multimedia Signal Processing, 2008, pp. 322-325.
[6] Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing, Pearson Prentice Hall, 2008.
[7] Van Otterloo, Peter J, A Contour Oriented Approach to Shape Analysis, New York Prentice Hall, 1991.
[8] Chi Cuong Tran, Dinh Tu Nguyen, Hoang Dang Le, Trong Hieu Luu, Quoc Bao Truong. Designing the Yellow Head Virus Syndrome Recognition Application for Shrimp on an Embedded System. The Interdisciplinary Research Journal 6 (2), 2019, pp. 48-63.
[9] Kostas Daniilidis, Reinhard Klette, Imaging Beyond the Pinhole Camera, Springer, 2006.