Infrared small target detection is a common task in infrared image processing. Under limited computational resources. Traditional methods for infrared small target detection face a trade-off between the detection rate and the accuracy. A fast infrared small target detection method tailored for resource-constrained conditions is proposed for the YOLOv5s model. This method introduces an additional small target detection head and replaces the original Intersection over Union （IoU） metric with Normalized Wasserstein Distance （NWD）， while considering both the detection accuracy and the detection speed of infrared small targets. Experimental results demonstrate that the proposed algorithm achieves a maximum effective detection speed of 95 FPS on a 15 W TPU， while reaching a maximum effective detection accuracy of 91.9 AP@0.5， effectively improving the efficiency of infrared small target detection under resource-constrained conditions.