Infrared image has the inherent defects of less effective information and low signal-to-noise ratio, which makes it challenging to discern targets from the background clutter in the spatial domain. To mitigate this challenge, we propose a multi-scale infrared detection model based on time-spatial feature fusion and YOLOv11n architecture. In particular, we design the PTF module to fuse spatial features, enabling the model extract robust spatiotemporal features of information complementarity while mitigating the impact of infrared noise on detection accuracy. Given the prevalence of background clutter in infrared images, which often generates erroneous feature points and degrades bounding box localization. Therefore, we developed EGF module to integrate edge information into multi-scale features to accurately target key feature points. Extensive evaluations on HIT-UAV public dataset confirm our model’s superiority, gaining 7.8% and 12.4% improvements in mAP50 and mAP50:95 against main-stream models.