Abstract: Infrared aerial object detection has been widely used in transportation, agriculture, military security, and other areas. The main challenges are small objects, mutual occlusion, little texture information, weak edge features, and large deformation of non-rigid bodies. To address these problems, based on YOLOv5 and structural Re-Parameterization (Rep), an improved object detection network Rep-YOLO is proposed for infrared aerial object detection. Firstly, the RepVGG module is introduced in the backbone network to improve the model feature extraction capability. During the model inference, the branches of the RepVGG module are structurally re-parameterized to reduce the branch and the complexity of the network structure. Secondly, the path aggregation network (PANet) in the neck of the detection network is improved by combining the priori feature, to increase the accuracy and speed balance capability. Finally, experiments are conducted on two publicly available infrared datasets, showing that the algorithm can effectively detect aerial infrared objects. Compared with the baseline (YOLOv5s), the statistical results on ComNet dataset show the mean Average Precision (mAP) is increased by 5.9%, while the parameters and model size are reduced by about 29.7% and 23.2%, respectively. In addition, the model deployment verification of our Rep-YOLO is carried out on the airborne platform Jetson Nano. It provides reliable technical support for the improvement of the detection algorithm and its practical application with UAV platforms.