In view of the infrared datasets which has limited scale and few labeled samples, a semi-supervised transfer learning method was proposed for the training of infrared object detection neural network. It aimed at improving the training efficiency and generalization ability of object detection neural networks on infrared datasets with limited scale, and increasing the adaptability of deep learning models in scenarios with few training samples such as infrared object detection. Firstly, the ability of unlabeled samples in improving model generalization and suppressing overfitting under few labeled samples was described. Then, the process of semi-supervised transfer learning for infrared object detection neural network was proposed: a pre-trained model was trained on large scale RGB dataset, and next it was fine-tuned using a few labeled and unlabeled IR images. Moreover, a pseudo-supervised loss function with feature similarity weighting was proposed, where the predictions from same batch was used as labels to each other, thus making full use of the feature distribution of similar objects in unlabeled images. To reduce the computation of semi supervised learning, the pseudo-supervised loss of object was limited on the objects within the neighborhood of its feature vector. Experimental results show that the test accuracy of object detection neural network trained by proposed method is higher than that trained by supervised transfer learning, it achieves an improvement of 1.1% on Faster R-CNN and a significant improvement of 4.8% on YOLO-v3, which verifies the effectiveness of the proposed method.