Owing to the infrared diffraction limit, the resolution of infrared remote sensing images is generally low, which makes precise detection and recognition of infrared targets difficult. To address this problem, an infrared target super-resolution detection algorithm based on deep learning is proposed. The algorithm comprises two main parts. The first part implements Wide Activation for Efficient and accurate image super-resolution(WDSR) to reconstruct infrared remote sensing images, and uses infrared images processed by the downsampling method of the sensor as the training set. The second part involves target detection based on Faster region-based convolutional neural network (Faster RCNN). A multiscale feature transfer network structure is proposed. The low-level features are input to region proposal network (RPN), which reduces the simplification rate of weak and small target pixels. In addition, a nonmaximum suppression method is used to reduce the suppression of dense target detection frames. Compared with Faster RCNN using the same training set, the proposed algorithm increased target detection accuracy, the overall recall rate, and the recall rate of small targets by 5.33%, 12.22%, and 13.25%, respectively.