IFMO-YOLOv11 : An Infrared Moving Small Object Detection Model Based on YOLOv11

In response to the multiple challenges faced in detecting small infrared moving targets—such as cluttered backgrounds, limited object size, weak feature representation, and low detection precision—this study introduces an enhanced detection model tailored for UAV infrared imagery. The proposed approach, named IFMO-YOLOv11, is built upon the YOLOv11 architecture and specifically optimized to improve the recognition of small infrared objects in aerial scenarios. Firstly, this model utilizes dilated convolution to design the multi-layer feature dilated convolution module (MLF-DC), which replaces the original SPPF layer to enhance the extraction of detailed features in UAV images. Secondly, to strengthen the C3K2 structure, the RFCBAMConv module is incorporated, refining internal convolutional mechanisms and feature integration, thereby boosting the model’s feature extraction performance. In addition, the integration of the Biformer module allows the network to better attend to critical details of small targets. To further enhance localization precision, an improved bounding box regression loss function—power-WIoU (P-WIoU), based on the upgraded Wise-IoU v3—is introduced for more accurate prediction box positioning. Through rigorous evaluation and testing on a publicly available dataset with wide recognition, extensive experimental results demonstrate that the proposed model achieves superior overall performance compared to other popular approaches. Notably, it offers a marked enhancement in detecting small, moving infrared targets.