To address the problem of poor object detection due to low contrast and inconspicuous object contours in infrared images in security scenes, an improved YOLOv7-based infrared security object detection algorithm is pro- posed. The recursive gated convolution is used to improve the backbone network and enhance the ability to interact with higher-order information of the input image; the ELAN-S module is constructed using the SimAM attention mech- anism to reduce the information loss rate while reducing the network parameters; the anchor box size is optimized using the K-means++ clustering algorithm to improve the detection accuracy. Data enhancement and experiments are con- ducted on the InfiRay public dataset, and the results show that the algorithm proposed in this paper has an mAP value of 87. 15% while maintaining a high detection speed, which is a significant improvement compared with the original YOLOv7 network and other mainstream algorithms, proving that the improved method is advanced and effective.