Abstract
Road cracks have a stimulating effect on the short-term and long-term life of pavements. For efficient maintenance of pavements, early detection of these cracks is imperative. The conventional methods for road condition assessments involve manual surveys that fail to meet the present-day requirements. As a result, there arises a need to use image-based approaches that can automatically detect the pavement conditions from the images of the roads. The current manuscript presents one such approach utilizing Gray-Level Co-occurrence Matrix (GLCM) and the associated features. In the proposed approach, the GLCM features are used to develop a road crack detection system based on machine learning techniques, capable of detecting the cracks, localizing them in the image, and providing valuable inputs related to the severity of the cracks present on a road section. The applicability of the proposed approach to separate cracks and non-cracks is assessed using images containing different types of cracks, captured using disparate mechanisms and from various locations (India, Japan, and China). The outcomes show promising results, proving the efficacy of the presented approach.
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Arya, D., Ghosh, S.K., Toshniwal, D. (2022). Automatic Recognition of Road Cracks Using Gray-Level Co-occurrence Matrix and Machine Learning. In: Gupta, D., Sambyo, K., Prasad, M., Agarwal, S. (eds) Advanced Machine Intelligence and Signal Processing. Lecture Notes in Electrical Engineering, vol 858. Springer, Singapore. https://doi.org/10.1007/978-981-19-0840-8_33
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DOI: https://doi.org/10.1007/978-981-19-0840-8_33
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