In highway transportation infrastructure such as highways and tunnels, the proportion of concrete consumption is the highest, and concrete cracks are common concrete problems. Concrete cracks will greatly affect the bearing capacity and safety of the structure, easily leading to the interruption of transportation lines, causing great economic losses, and endangering personnel safety. Therefore, the effective identification and timely reporting of concrete cracks is of great significance for the maintenance of infrastructure such as roads and tunnels. In this paper, the CaNet, a deep learning network for identifying concrete cracks, is proposed, which takes ResNet50 as the backbone network. In order to capture the area with a small proportion of cracks, we added coordinate attention to the residual unit of ResNet50 to capture the cross-channel information, direction-aware information, and position-sensitive information from many vertical and horizontal directions so that the network can more accurately locate the narrow crack area. In experiments 3.2 and 3.3, the CaNet has an accuracy rate of 89.6%, which is higher than that of the compared network. In addition, the recall, F1 score, and precision of the CaNet network are 86%, 85%, and 87% , respectively. Therefore, the CaNet model is effective for identifying concrete cracks.

A low-quality unwinding process of the unwinding machine can lead to downtime of the knitting machine and loss of fabric quality. To address this issue, we studied the tension and vibration of carbon fiber yarns during unwinding. Based on the theory of axially moving strings, the dynamic model of carbon fiber yarn was established during the unwinding process, bringing the parameters into the simulation. According to the simulation results, the yarn tension in the unwinding process is closely related to the spring preload, which needs control within a certain range. When the unwinding speed increases, the fluctuation amplitude, lateral vibration amplitude, and axial vibration amplitude of the yarn tension gradually increase, causing friction and wear of the carbon fiber yarn. By controlling factors such as spring preload, unwinding speed, and the number of bobbins unwound simultaneously, one can effectively control the yarn’s vibration and improve the quality of the yarn.