Ground infrared target detection is a key technology in the fields of camouflage protection and precision guidance. For the current deep learning-based target detection model to detect infrared targets in the ground background, it is easy to be interfered by complex backgrounds and insufficient attention to the target, which leads to the problem of low detection accuracy. A method of ground infrared target detection based on a parallel attention mechanism was proposed. Firstly, the parallel down-sampling method of convolution and attention was used to reduce the spatial complexity of the model and increase the training speed, while focusing and paying attention to the target features. Secondly, the multi-scale features extracted by the backbone network were fused to suppress the interference of background information and improve the accuracy of target detection through the multiplexing and complementary of different scale information. Finally, the focal loss and CIOU loss were used to improve the classification and regression accuracy of the model. The experiment results showed that the average detection accuracy of the model on the Infrared-VOC dataset was 82.2%, which was 6.9% higher than YOLOv3. At the same time, the space complexity of the model was only 32.6% of YOLOv3, and the training time was 43.7% of YOLOv3. The improvement of model training efficiency and detection accuracy was achieved.