The intelligent crack detection method is an important guarantee for the realization of intelligent operation and maintenance, and it is of great significance to traffic safety. In recent years, the recognition of road pavement cracks based on computer vision has attracted increasing attention. With the technological breakthroughs of general deep learning algorithms in recent years, detection algorithms based on deep learning and convolutional neural networks have achieved better results in the field of crack recognition. In this paper, deep learning is investigated to intelligently detect road cracks, and Faster R-CNN and Mask R-CNN are compared and analyzed. The results show that the joint training strategy is very effective, and we are able to ensure that both Faster R-CNN and Mask R-CNN complete the crack detection task when trained with only 130+ images and can outperform YOLOv3. However, the joint training strategy causes a degradation in the effectiveness of the bounding box detected by Mask R-CNN.

China’s high-speed railway (HSR) construction industry has been experiencing a golden period of opportunities in the recent decades. Due to the necessity of protecting arable lands and the advantage of rapid construction, bridges (elevated structures) are predominantly used in China’s HSRs, which must meet the critical small settlement requirement. This article first reviews the main technical standards of small- to medium-span HSR bridges, where their comparison with the Japanese standard and International Union of Railway standards is highlighted. Then, the long-span HSR steel bridges are described and discussed case by case. Based on the engineering practice of HSRs, the main achievements and key technologies of HSR bridges in China are provided, followed by the opportunities and challenges of the current and future developments of HSR bridges.