Ground infrared target detection is an important research content in the fields of target reconnaissance, intelligent perception and camouflage protection. Aiming at the target detection model based on anchor frame, it needs the guidance of anchor frame when extracting features, which will produce a large number of calculation parameters related to anchor frame, and lead to inaccurate detection, poor generalization performance and easy to miss detection. Based on the target detection model without anchor frame based on the idea of image segmentation, a backbone network based on deformable convolution for feature extraction is constructed, and the convolution kernel is used to adapt the target shape to enhance the network's extraction effect on target features. Combining the attention mechanism of space and channel, focus on the target from the spatial dimension and channel dimension, realize the three-dimensional attention to the target feature, and improve the target information acquisition ability of the target detection model. The proposed ground infrared target detection model reached a detection accuracy of 91.3% on the Infrared-VOC dataset, and the overall performance of the ground infrared target detection model based on the Anchor-Free frame is optimized.