红外技术2025，Vol.47Issue(10)：1263-1271,9.
UAV-YOLO:红外场景下无人机实时目标检测算法
UAV-YOLO:A Real-Time Target Detection Algorithm For UAVs In Infrared Scenes
摘要
Abstract
An improved unmanned aerial vehicle(UAV)-YOLO algorithm is proposed to address the problems of low precision and high computation of UAV detection in infrared scenes.First,a weighted bidirectional feature pyramid network(BiFPN)was introduced to enhance the model detection performance by optimizing multi-scale feature fusion.Second,a lightweight shared detail enhanced convolutional detection head(LSDECD)was used to enhance the performance of small-target detection while decreasing the number of parameters,and a convolution and attention fusion module(CAFM)was constructed to strengthen the feature interactions to enhance the robustness.Finally,a Wise-SIoU loss function was used to accelerate model convergence.The experimental results demonstrate that the improved model achieves 91.3％mAP@50,with a 1.7％enhancement compared to the original YOLOv11n.Validation under a weak aircraft target detection and tracking dataset of public infrared images shows that the improved model improves all evaluation indices,proving that it has good generalization and robustness.关键词
无人机检测/金字塔网络/检测头/卷积注意力/Wise-SIoU/检测平均精度Key words
UAV detection/pyramid network/detection head/convolutional-attention/wise-SioU/detection average accuracy分类
计算机与自动化引用本文复制引用
刘清荣,陈慈发,张上..UAV-YOLO:红外场景下无人机实时目标检测算法[J].红外技术,2025,47(10):1263-1271,9.基金项目
湖北省大学生创新创业训练计划(202311075046) （202311075046）
国家大学生创新创业训练计划(202111075012,202011075013) （202111075012,202011075013）
湖北省中央引导地方专项(2024BSB002). （2024BSB002）
