为解决现有路面裂缝检测算法精度低、漏检率高等问题，提出了一种基于YOLOv8n改进的路面裂缝检测算法YOLO-CD（YOLO-crack detection）。YOLO-CD算法采用ASF-YOLO架构中的尺度序列特征融合（scale sequence feature fusion, SSFF）模块和三重特征编码器（triple feature encoder, TFE）模块，提高了对多尺度裂缝的检测性能和对目标特征的感知能力。同时，在骨干网络末端和颈部网络中引入CA注意力机制（coordinate attention），将位置信息嵌入通道注意力中，强化了对裂缝特征的提取能力。此外，在YOLOv8n原有3个输出层的基础上新增了一个P2小目标检测层，增加了网络的多尺度感受野，可以同时捕获全局和局部上下文信息，提升了算法在复杂场景中对小目标裂缝的检测能力。通过将原始YOLOv8n的检测头替换为DyHead检测头，使尺度、空间和任务3种注意力机制结合统一，进一步提升了网络对裂缝的检测性能。实验结果表明，在自建数据集PD-Dataset中，改进后的算法YOLO-CD比原算法YOLOv8n的mAP50提高了4.1%。在公共数据集RDD2020中，改进后的算法YOLO-CD比原算法YOLOv8n的mAP50提高了1.5%。且算法检测速度达到了89.9帧／s，满足了路面裂缝检测实时性的要求。

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.