针对现有语义分割方法检测高速公路护栏时存在预测速度慢、分割精度低的问题，提出一种基于改进DeepLabV3+的无人机高速公路护栏检测方法。首先，采用MobileNetv2网络替换原模型的主干并输出中层特征，减少参数量的同时恢复降采样过程中丢失的空间信息；然后，采用密集连接扩张卷积改进空洞空间金字塔池化，以减少漏分割现象；最后，在编码器部分引入空间分组增强（SGE）注意力机制，减少错分割现象。实验结果表明，改进后模型平均交并比、平均像素准确率、画面每秒传输帧数达到了79.20%、87.89%、52.59，相比基础模型，分别提高了2.59%、2.93%、56.70%，参数量降低了78.85%，能够在保障模型预测速度的同时提高对护栏的分割精度。

To address the problems of slow prediction speed and low segmentation accuracy of existing semantic segmentation methods for highway guardrail detection, an UAV highway guardrail detection method based on improved DeepLabV3+ is proposed. First, the MobileNetv2 network was used to replace the backbone of the original model and outputs the middle layer's features to reduce number of parameters while recovering the spatial information lost in the downsampling process; then an atrous spatial pyramid pooling was improved by the densely connected expansive convolution to reduce the phenomenon of missed segmentation; finally, the spatial group-wise enhance (SGE) attention mechanism was introduced in the encoder part to reduce the phenomenon of wrong segmentation. The experimental results show that the average intersection over union, average pixel accuracy, and frames per second transmission of the improved model can reach 79.20%, 87.89%, and 52.59, which are 2.59%, 2.93%, and 56.70% higher than the base model, respectively, and number of parameters is reduced by 78.85%. This method can thus improve the segmentation accuracy for the guardrail while guaranteeing the model's prediction speed.