In response to the problems of traditional road crack detection methods, such as time-consuming, laborintensive, high cost, and subjectivity, a YOLOv5-based road crack detection model, named YOLOv5-Crack, is proposed. Firstly, a coordinate attention mechanism is introduced in the backbone and optimized as a CA-plus structure to enhance the crack feature focus. Secondly, a novel feature fusion network ESPP is proposed to reduce some computational costs while improving the feature fusion capability. Then, the heavy Ghost-Shuffle convolution is used in the neck network to replace the traditional convolution, which can keep the channel semantic information as much as possible while reducing computational costs. Finally, the SIoU loss function is introduced to improve the regression accuracy. To validate the effectiveness of the improved YOLOv5-Crack model, comparative experiments are conducted on the GRDDC 2020 dataset, and the results show that the F1 scores are 58.43% and 58.21%, respectively, which are 4.05% and 3.93% higher than those of the original YOLOv5 model, and the computational cost is reduced by 7.8 GFLOPs, with an FPS of 37.9, effectively addressing the shortcomings of road crack detection. Furthermore, compared with mainstream object detection algorithms, the proposed YOLOv5-Crack model has superior performance in road crack detection.