Abstract
Steep roads with potholes, irregular slopes, and other barriers affect vehicle safety and accident prevention in mountainous areas. The paper proposes a computationally efficient computer vision and machine learning-based approach to detect these roads. The dataset includes damaged, steep, and narrow mountainous roadways. The Field of View (FoV) creation step in preprocessing increased the algorithm’s accuracy. The five statistical texture parameters—Entropy, Correlation, Homogeneity, Contrast, and dissimilarity—are extracted using the Gray-Level Co-Occurrence Matrix (GLCM) technique. The accuracy of various combinations of features, orientations, and distances varied. Haralick’s five horizontal, diagonal, and vertical GLCM-based features provided 90.95% accuracy. Experimentally, the Light Gradient Boosted Machine (LGBM) classifier predicted mountain roads most accurately.
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Madake, J., Vyas, A., Pawale, V., Chaudhary, V., Bhatlawande, S., Shilaskar, S. (2023). Vision-Based Driver Assistance System to Detect Mountainous Roads Using GLCM and Haralick Texture Features. In: Rao, U.P., Alazab, M., Gohil, B.N., Chelliah, P.R. (eds) Security, Privacy and Data Analytics. ISPDA 2022. Lecture Notes in Electrical Engineering, vol 1049. Springer, Singapore. https://doi.org/10.1007/978-981-99-3569-7_10
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DOI: https://doi.org/10.1007/978-981-99-3569-7_10
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