A hybrid algorithm for aeolian saltating particle recognition under low-medium particle concentrations with Gaussian Mixture Model， Support Vector Machine and probability-distribution of saltating particles' geometric and color parameters

doi:10.7522/j.issn.1000-694X.2025.00261

Journal of Desert Research ›› 2026, Vol. 46 ›› Issue (3): 255-261.DOI: 10.7522/j.issn.1000-694X.2025.00261

A hybrid algorithm for aeolian saltating particle recognition under low-medium particle concentrations with Gaussian Mixture Model， Support Vector Machine and probability-distribution of saltating particles' geometric and color parameters

Haoqiang Li(), Huijuan Li(), Qianwen Yang, Fanmin Mei

School of Environmental and Chemical Engineering，Xi'an Polytechnic University，Xi'an 710048，China

Received:2025-04-28 Revised:2025-10-21 Online:2026-05-20 Published:2026-06-11
Contact: Huijuan Li

Abstract

Abstract:

Current algorithms for saltating particle recognition face challenges in balancing accuracy and computational efficiency. To address this issue， this study proposes an integrated algorithm combining Gaussian Mixture Model （GMM）， Support Vector Machine （SVM） classification， and a statistical feature verification model for sand particle confirmation. Experimental results on sand image recognition demonstrate that the proposed method achieves a moderate recall rate （60%-90%）， the highest accuracy （80%-95%） among existing methods， and significantly reduced computational time. The work provides a new idea and method for the recognition and tracking of aeolian sand images at low to medium concentrations.

Key words: sand particle, Gaussian Mixture Model, Support Vector Machine, probability-distribution

CLC Number:

Q151.94

Haoqiang Li, Huijuan Li, Qianwen Yang, Fanmin Mei. A hybrid algorithm for aeolian saltating particle recognition under low-medium particle concentrations with Gaussian Mixture Model， Support Vector Machine and probability-distribution of saltating particles' geometric and color parameters[J]. Journal of Desert Research, 2026, 46(3): 255-261.

Figures/Tables 6

Fig.1 Workflow of the new hybrid scheme for saltating particle recognition

Fig.2 The number of saltating particles identified by Gaussian mixture model varies with the kernel shape and size of the opening and closing for morphological operation； The F1 score of Gaussian mixture model fluctuates with the sand particle concentrations under the various combinations of the kernel shape-size parameters

Fig.3 Probabilities of widths， heights， perimeters and areas of saltating particles from the L3 video section

Table 1 Color criterions for identifying saltating particle

跃移沙粒颜色特征	颜色空间分量
R	0~48
G	16~96
B	0~48
H	0~89
S	0~160
V	17~96

Fig.4 Comparisons between the new algorithm and other published schemes for saltating particle recognition in recall and accuracy rates （GT， DGT、 GMM、 GMM+SVM correspond to the gray threshold scheme from Matlab ［10］， dynamic threshold scheme ［14］， Gaussian mixture model ［16］ and improved Gaussian mixture model. The data of YOLOv5 and YOLOv8 derive from the Ref.12 and the Ref.11， respectively）

Table 2 Inference time of the new scheme and the published schemes for saltating particle recognition

算法	运行配置	每帧用时/ms
YOLOv5	RPX 4090	4.2
Sand-YOLOv5	RPX 4090	6.1
YOLOv8	Intel Core i5-8300H	94.5
GMM	Intel Core i5-7300HQ	6.5
GMM+SVM	Intel Core i5-7300HQ	7.9
新算法	Intel Core i5-7300HQ	10.2

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A hybrid algorithm for aeolian saltating particle recognition under low-medium particle concentrations with Gaussian Mixture Model， Support Vector Machine and probability-distribution of saltating particles' geometric and color parameters

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