A semi-supervised target detection algorithm for infrared images based on CenterNet and OMix enhancement (IRCC-OMix) is proposed to improve target detection accuracy. The prior information of the anchor frame in the infrared image is difficult to determine. Therefore, CenterNet is used as the backbone model to detect the target in the infrared image through key points. A semi-supervised learning method based on teacher-student-network mutual learning is introduced owing to the high cost of infrared image annotation, and a semi-supervised infrared image target detection (IRCC) model based on CenterNet and consistency is designed. The random erasure enhancement in the IRCC model may lead to the disappearance of small targets in the infrared image, which affects the detection performance of the model. Therefore, an object-based image mixing enhancement method is adopted to improve the detection ability of the algorithm for small targets. The experimental results on the public dataset, FLIR, show that the average precision mean (mAP) of the IRCC model reaches 55.3%, which is 1.9 percentage points higher than that of the training using only labeled data. This indicates that the model can fully utilize unlabeled data and improve its robustness. The mAP of the OMix-enhanced IRCC model is 56.8%, which is 1.5 percentage points higher than that of the cutout-enhanced IRCC model and achieves good detection performance.