A new deep convolutional network for detecting small targets in infrared images is proposed. The problem of small targets detection is transformed into the classification of small targets’ location distribution. First, a Fully Convolutional Networks is used for enhancing and initially screening the small targets. After that, the original image and the background suppressed image are selected as the inputs for classification network which is used for the follow-up screening, and then the SEnet (Squeeze-and-Excitation Networks) is used to select the feature maps. The experimental results show that the detection network is superior to multiple typical infrared small target detection methods and has an excellent result on different signal-to-noise ratio,different scenes and motion blur targets.