无人机被广泛应用于救援、物流、巡检等场景，在飞行过程中及时检测障碍物是安全完成任务的前提保障。为了满足无人机飞行中对障碍物检测的要求，以YOLOv5算法为基础提出一种改进算法。使用卷积模块代替Focus模块并在原有网络结构中增加一个检测头，以提高检测性能；在特征提取网络中融合CA（coordinate attention）注意力机制以加强模型特征提取能力，降低背景干扰。实验结果表明，改进的算法相比原YOLOv5模型精确率提升了13.8%，召回率提升了12%，mAP@0.5提升了15.4%，mAP@0.5:0.95提升了10.2%，证明了改进算法的优越性。

UAV are widely used in rescue, logistics, inspection and other scenarios, and timely detection of obstacles during flight is a prerequisite for safe completion of tasks. In order to meet the requirements of obstacle detection in UAV flight, an improved algorithm was proposed based on YOLOv5 algorithm. The convolution module was used instead of the Focus module and a detection probe was added to the original network structure to improve the detection performance. The CA (coordinate attention) attention mechanism was integrated in the feature extraction network to strengthen the feature extraction ability of the model and reduce background interference. Experimental results show that compared with the original YOLOv5 model, the improved algorithm improves the accuracy by 13.8%, the recall rate by 12%, the mAP@0.5 by 15.4%, and the mAP@0.5:0.95 by 10.2%, which proves the superiority of the improved algorithm.