In order to address the issues of low accuracy and high missed detection rates in existing pavement crack detection algorithms, an improved pavement crack detection algorithm based on YOLOv8n, named YOLO-CD (YOLO-crack detection), has been proposed. The scale sequence feature fusion (SSFF) module and triple feature encoder (TFE) module from the ASF-YOLO architecture were utilized by the YOLO-CD algorithm to enhance the detection performance for multi-scale cracks and the perception capability of target features. Additionally, the coordinate attention(CA) mechanism was introduced at the end of the backbone network and in the neck network, with positional information embedded into channel attention, thereby strengthening the extraction capability of crack features. Furthermore, an additional P2 small object detection layer was added on top of the original three output layers of YOLOv8n, increasing the multi-scale receptive field of the network, allowing both global and local context information to be captured simultaneously, thereby improving the detection capability for small cracks in complex scenes. The original YOLOv8n detection head was replaced by the DyHead detection head, achieving the integration of scale, spatial, and task attention mechanisms, and further enhancing the network’s detection performance for cracks. Experimental results show that in the self-built PD-Dataset, the mAP50 of the improved YOLO-CD algorithm is increased by 4.1% compared to the original YOLOv8n algorithm. In the public dataset RDD2020, the mAP50 of the improved YOLO-CD algorithm is increased by 1.5% compared to the original YOLOv8n algorithm. Moreover, the algorithm’s detection speed is found to reach 89.9 frames/s, meeting the real-time requirements of pavement crack detection.