Segmentation method of drivable areas of mine roads based on rut texture features

Xinyuan Yuan; Kaiqi Huang; Yunzhen Xiong

doi:10.1117/12.3025775

1 April 2024 Segmentation method of drivable areas of mine roads based on rut texture features

Xinyuan Yuan, Kaiqi Huang, Yunzhen Xiong

Proceedings Volume 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023); 130810Q (2024) https://doi.org/10.1117/12.3025775
Event: 2023 3rd International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 2023, Tianjin, China

Abstract

The mine road is unstructured, the line shape is changeable, and the color of the road surface is similar to that of the mountains on both sides. In order to make up for the deficiency of the segmentation effect of the road detection model based on shape and color, a driving area segmentation method based on rut texture features is proposed. Initially, the collected images are preprocessed in the region of interest. Secondly, the gray level co-occurrence matrix (GLCM) is used to obtain the characteristic parameters of the rutting area, and four texture feature indexes are used as the input feature vectors of the genetic algorithm (GA) to obtain the optimal segmentation threshold. Finally, by filling the hole noise in the segmented image, a complete drivable area is obtained. The comparison test shows that the proposed segmentation method based on rut texture features can effectively overcome the problem that the road is similar to the background color and is difficult to segment. Compared with other similar segmentation methods, the segmentation effect of the road has better accuracy and robustness.

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

Citation Download Citation

Xinyuan Yuan, Kaiqi Huang, and Yunzhen Xiong "Segmentation method of drivable areas of mine roads based on rut texture features", Proc. SPIE 13081, Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023), 130810Q (1 April 2024); https://doi.org/10.1117/12.3025775

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KEYWORDS

Image segmentation

Roads

Mining

Cooccurrence matrices

Tunable filters

Genetic algorithms

Image processing algorithms and systems

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