Monitoring road conditions is crucial for safe and efficient transportation infrastructure, but
develo** effective models for automatic road damage detection is challenging requiring …

The data article describes the Road Damage Dataset, RDD2022, encompassing of 47,420
road images from majorly six countries, Japan, India, the Czech Republic, Norway, the …

Automatic image-based crack detection of concrete bridge decks contributes to safer bridge
operation and bridge health monitoring. Existing models suffer from overfitting and low …

With the development of modern society, traffic volume continues to increase in most
countries worldwide, leading to an increase in the rate of pavement damage Therefore, the …

Abstract Machine learning techniques have been used to increase detection accuracy of
cracks in road surfaces. However, most studies failed to consider variable illumination …

Crashes and delays at Railroad Highway Grade Crossings (RHGC), where highways and
railroads intersect, pose significant safety concerns for the US Federal Railroad …

This study presents a CNN-based model for detecting cracks in concrete images. Even
though the model is simple and has only a few layers, it provides significant accuracy. This …

This paper proposes an integrated framework for automatically segmenting road surface
cracks that utilize a Multi-Attention-Network and a modified U-Net, combined through neural …