针对当前道路裂缝检测算法检测速度慢、且对于复杂道路条件下的路面裂缝识别精度低、误检漏检率高等问题，设计了一种基于YOLOv8的道路裂缝检测改进算法FCG-YOLO。首先，提出了改进的C2f_Faster结构以降低模型参数量和减少计算复杂度。其次，为了提高检测准确率，在骨干部分引入全局注意力机制GAM，增强算法对小目标裂缝的识别能力，同时改进特征融合金字塔SPPF为SPPFCSPC模块，用于增强算法的特征融合能力。最后，通过消融实验验证了各模块对算法性能的影响，找到了兼具轻量性与准确性的模型配置，并结合实际应用场景，探究了算法的鲁棒性与泛化性。FCG-YOLO在检测效率上表现出色，验证集上的检测精度mAP50与mAP50-95分别达到了90.3%、74.4%，且每秒检测速度提升13.8%，达到345帧，具备较高的检测效率与良好的实际应用价值。

Addressing challenges such as large memory footprint, high computational complexity, and insufficient real-time detection speed in road crack detection models for complex scenarios, a highly efficient and precise algorithm named FCG-YOLO was proposed. Lightweight modules and attention mechanisms were integrated, and traditional feature fusion pyramids were enhanced. The algorithm incorporates PConv into the residual calculation module of YOLOv8n to introduce the improved C2f_Faster structure, thereby reducing model parameters and computational complexity. To enhance detection accuracy, GAM (global attention mechanism) was introduced into the backbone, and the Feature Fusion Pyramid SPPF was improved to SPPFCSPC module, enhancing the model's ability to represent and fuse features of road cracks. The impact of each module on algorithm performance was verified through ablation experiments, identifying a lightweight and accurate model configuration. Furthermore, the robustness and generalization of the algorithm were explored in practical application scenarios. FCG-YOLO demonstrates outstanding detection efficiency, achieving a detection accuracy of 90.3% mAP50 and 74.4% mAP50-95 on the validation set, with a detection speed of 345 frames per second. These results highlight its high detection efficiency and significant practical value.