Abstract: This study proposes an intelligent method for recognizing the lithology of a tunnel working face by combining a convolutional neural network and visual transformer. First, an efficient method for collecting high-resolution ...

Abstract: The rail running band is a mathematical representation describing the continuous strip-shaped spatial surface resulting from the rolling contact operation of train wheels on the rail surface, which establishes a direct ...

Abstract: Convolutional neural networks (CNNs) have achieved tremendous success in pavement crack segmentation. However, it is difficult for CNN-based crack segmentation methods to minimize false-negative and false-positive errors. ...

Abstract: Accurately identifying sealed cracks on asphalt pavement surfaces is of significant importance to pavement management. This paper proposes an efficient semantic segmentation model called Parallel-TDNet for pixel-level ...

Abstract: This paper introduces DLANet, an algorithm for semantic segmentation designed to enhance the pixel-level detection of sealing cracks. DLANet extends the DeepLabV3+ encoder-decoder architecture by integrating four different ...

Abstract: Accurate recognition and location of pavement manholes are of great significance for pavement maintenance. This paper proposes an improved You only look once X (YOLOX) for automated detection of manholes on asphalt pavements. ...

Abstract: Precise crack identification on intricate asphalt pavements poses a significant challenge for intelligent pavement distress detection. This paper proposes the CellNet, a deep learning model designed to offer an effective ...

Abstract: Image-based systems are becoming popular to collect pavement condition data for pavement management activities. Pavement engineers define various distress categories based on pavement types. However, software solutions ...