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The infrared dim-small target detection is a key technology in the fields of security surveillance, reconnaissance detection and precision guidance. To improve the accuracy and real-time performance of infrared dim-small target detection under complex background conditions, an infrared dim-small target detection algorithm YOLO-FCSP based on deep learning was proposed. Considering the characteristics of dim-small targets in infrared images, the feature extraction network was designed based on the YOLO detection framework by reducing the number of downsampling and combining cross-stage local modules, Focus and spatial pyramidal pooling structure. The feature fusion network was improved by utilizing the idea of multi-path aggregation. The number of detection output layers was adjusted to enhance the reuse of feature information. The experimental results show that the proposed method has higher accuracy and detection speed in detecting infrared dim-small targets, achieving 91.9% precision and 94.6% recall, the average precision (AP) value to 92.6%, and the detection speed as 170 fps, which meet the requirements of real-time detection in practical applications.