Work

Image-Enhanced Asphalt Crack Detection Research

Year: 2025

Type: article

Abstract: The main environmental factors that interfere with asphalt pavement crack detection include shadows from ambient light of different intensities, trees, signboards, railings, etc. Traditional crack detection methods usually eliminate the effect of light during pre-processing or improve the recognition results by local consistency and light normalization during image segmentation. However, the current methods can only improve the image brightness uniformity and cannot completely eliminate the effect of light, so the usual methods are only effective when the image brightness uniformity is good. So, this study puts out an approach to improving images. Machine learning is used to suggest a method for detecting cracks in asphalt pavement, with the help of an attention mechanism, using photos of real roads as the experimental dataset. Experiments show that the method can preprocess image data well and enhance the robustness of training in machine learning structures. Test results show that our method can be well applied to practical testing work.

Source: International Journal of High Speed Electronics and Systems

DOI: 10.1142/s0129156425404218

Authors: Wangping Ye, Guibin Wu, Hao Peng, Chengzhong Wu

Institution: Chizhou University

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Funder: Chizhou University