红外小目标检测是红外图像处理中的一项常见任务.在计算资源受限的条件下,传统的红外小目标检测方法面临着检测率和检测精度的平衡问题.本文针对 YOLOv5s 模型提出了一种在资源受限条件下快速红外小目标检测方法,该方法增加了一个小目标检测头,并用Normalized Wasserstein Distance(NWD)度量取代了原来的Intersection over Union(IoU)度量,同时考虑了红外小目标的检测精度和检测速率.实验结果表明,改进后的算法在 15 W TPU 上实现了最大 95 FPS 的红外小目标有效检测速度,同时达到了最大 91.9 AP@0.5的检测精度,有效提高了资源受限条件下的红外小目标检测效率.
Research on fast detection method of infrared small targets under resource-constrained conditions
Infrared small target detection is a common task in infrared image processing.Under limited computa-tional 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 pro-posed 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 reach-ing a maximum effective detection accuracy of 91.9 AP@0.5,effectively improving the efficiency of infrared small target detection under resource-constrained conditions.
infrared UAV imagefast small object detectionlow impedanceloss function
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