针对现有的路面病害检测算法在复杂环境下检测精度低、模型复杂度高的问题，在YOLOv5基础上，提出了一种融合可变形卷积的轻量级路面病害检测算法（lightweight deformable convolution YOLOv5, LDC-YOLOv5）。首先，针对真实路面病害复杂不规整的特点，使用可变形卷积（Deformable Conv）和深度卷积（Depthwise Conv），设计了一种轻量级特征提取模块，代替原网络主干部分的C3模块，使卷积核聚焦在无规则裂缝病害上，增强病害特征提取能力。其次，针对特征融合阶段出现算法复杂度过高的问题，使用轻量级卷积GhostConv，构建一种轻量级特征融合模块，代替原网络颈部网络部分的C3模块，降低网络参数和复杂度；为避免真实路面出现光照不均，出现阴影遮挡路面病害目标而造成的病害漏检的情况，在主干网络部分，引入轻量级注意力机制TripletAttention，增强算法对病害信息上下文之间的理解能力。最后在IEEE公开数据集RDD2022和Kaggle公开数据集Road Damage上进行测试，实验结果表明，与YOLOv5s相比，mAP50在两个数据集上分别提升了1.4%和4.2%，且模型参数量仅为YOLOv5s的67.6%。

In response to the low detection accuracy and high model complexity of existing road damage detection algorithms in complex environments, a lightweight road damage detection algorithm named LDC-YOLOv5 (lightweight deformable convolution YOLOv5) was proposed based on YOLOv5. To address the complexity of real road surface damages, a lightweight feature extraction module was designed using Deformable Conv (deformable convolution) and Depthwise Conv (depthwise convolution) to replace the C3 module in the original network backbone, enabling convolutional kernels to focus on irregular crack damages and enhancing feature extraction for damage detection. To reduce algorithm complexity in the feature fusion stage, a lightweight feature fusion module was constructed using GhostConv to replace the C3 module in the original network neck, lowering network parameters and complexity. Additionally, to prevent missed detections caused by uneven lighting and shadow obstruction, a lightweight attention mechanism, TripletAttention, was introduced in the backbone network to improve the algorithm's understanding of damage information and context. Experiments conducted on the IEEE open dataset RDD2022 and the Kaggle open dataset Road Damage demonstrate that, compared to YOLOv5s, the proposed LDC-YOLOv5 achieves a 1.4% and 4.2% improvement in mAP50 on the two datasets, respectively, with only 67.6% of the model parameters of YOLOv5s.