In order to improve the detection accuracy of infrared targets, a Faster R-CNN infrared target detection algorithm introducing a frequency domain attention mechanism was proposed. Firstly, a parallel image enhancement preprocessing structure was designed to address the issues of edge blur and noise in infrared images. Secondly, a frequency domain attention mechanism was introduced into Faster R-CNN, and a new infrared target detection backbone network was designed. Finally, a path enhanced pyramid structure was introduced to fuse multi-scale features for prediction, and the rich location information of the underlying network was utilized to improve detection accuracy. The experiment was conducted on a dataset of infrared aircraft. The results show that the AP of improved Faster R-CNN target detection framework is 7.6% higher than that of the algorithm with ResNet50 as the main stem. In addition, compared with current mainstream algorithms, the proposed algorithm improves the detection accuracy of infrared targets and verifies the effectiveness of the algorithm improvement.