Detection of pass rate of high vertical sand barrier of reed based on K-means clustering

Yingchao Wang; Na Li; Yuhua Ma; Shan Sun; Jiangyu Zhang

doi:10.1117/12.3026334

27 March 2024 Detection of pass rate of high vertical sand barrier of reed based on K-means clustering

Yingchao Wang, Na Li, Yuhua Ma, Shan Sun, Jiangyu Zhang

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131051K (2024) https://doi.org/10.1117/12.3026334
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

In order to solve the problem that it is difficult to quickly detect the pass rate of the reed high vertical sand barrier after it is made, a method for the pass rate detection of the high vertical sand barrier of reed based on K-means clustering was proposed. The images of the reed high vertical sand barrier were collected and preprocessed, the K-means clustering algorithm was used to cluster the reed bundle area in the image, and the separation of the background and the reed bundle area in the image was completed by the binarization operation. Canny edge detection and minimum circumscribed rectangle operation were performed on the binary image, the midline of the smallest circumscribed rectangle was obtained, and the distance between the reed bundles was calculated with the help of Euclidean distance. Experimental results show that this method can effectively detect the gap of the reed high vertical sand barrier, improve the speed of pass rate detection, and provide a new technical means for desert control.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yingchao Wang, Na Li, Yuhua Ma, Shan Sun, and Jiangyu Zhang "Detection of pass rate of high vertical sand barrier of reed based on K-means clustering", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131051K (27 March 2024); https://doi.org/10.1117/12.3026334

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KEYWORDS

Sand

Image segmentation

Tunable filters

Digital filtering

Edge detection

Image filtering

Binary data

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