针对无人机航拍公路图像坑槽检测中的目标多尺度变化显著和复杂场景导致的检测精度不足问题，提出了一种改进YOLOv11的无人机航拍图像坑槽检测算法。首先，网络主干部分采用轻量化增强检测模块（LEDM_module）代替原有C3K2特征提取模块，通过使用分组并行处理方法，对多尺度坑槽特征通道进行分割，并采用自适应特征增强的形式动态提取坑槽关键信息，利用轻量化计算和冗余参数消除的结合来提高坑槽特征的提取精度和模型的运行效率。其次，在网络颈部使用增强多尺度注意力融合模块（EMSA_module）代替基于上采样级联和卷积的原始特征融合方法，通过结合坑槽特征权重的动态注意力校准、坑槽边缘分组空间细化和残差特征融合，提高小坑槽特征稀释和大坑槽特征与背景混淆下的跨尺度信息传输效率。实验结果表明，改进后模型mAP@50和mAP@0.5-0.95分别为86.6%和58.3%，相较基准模型YOLOv11n分别提升5.74%和11.69%；召回率为82.7%，相较基准模型提升19.68%。实验结果证明，本文提出的优化策略可以有效提高模型对多尺度弱特征坑槽的检测能力，降低高速公路无人机航拍图像坑槽检测任务中的漏检率。

To address the issues of significant multi-scale variations in target and insufficient accuracy in complex scenarios during the highway potholes detection in aerial images by UAV, an improved YOLOv11 algorithm for aerial highway potholes detection is proposed. Firstly, a lightweight enhanced detection module (LEDM_module) is designed in the backbone network to replace the original C3K2 feature extraction module. By using the grouped parallel processing method, the multi-scale pothole feature channels are segmented, and the key information of potholes is dynamically extracted in the form of adaptive feature enhancement. The combination of lightweight computing and redundant parameter elimination is utilized to improve the extraction accuracy of pothole features and the operational efficiency of the model. Secondly, the enhanced multi-scale attention fusion module (EMSA_module) is used in the neck network to replace the original feature fusion method,which based on upsampling concatenation and convolution. By integrating dynamic attention calibration of pothole feature weights, grouped spatial refinement of pothole edges, and residual feature fusion, the cross-scale information transmission efficiency is enhanced under the conditions of diluted small pothole features and confusion between large pothole features and background. The experimental results show that the mAP@50 and mAP@0.5-0.95 of the improved model are 86.6% and 58.3%, respectively, compared with the baseline YOLOv11n, the above indicators have improved by 5.74% and 11.69%, respectively. The recall rate is 82.7%, compared with YOLOv11n, the indicator has improved by 19.68%. The experimental results prove that the optimization strategy proposed in this paper can effectively improve the detection ability for multi-scale and weak features potholes, and reduce the missed detection rate for the task of highway potholes detection in aerial images by UAV.