基于IDOU-YOLO的红外图像无人机目标检测算法

Infrared image UAV target detection algorithm based on IDOU-YOLO

低空小型无人机(unmanned aerial vehicle,UAV)侵扰敏感区域事件频发,使国家和社会面临严重安全威胁。针对基于热成像的无人机目标检测存在漏检率高、检测精度不足的问题,提出了IDOU-YOLO(infrared detection of UAV-YOLO)算法模型,通过构建多尺度融合特征金字塔机制,充分挖掘特征空间信息,聚焦尺度的信息融合及丰富模型的信息表征能力,增强目标检测能力;同时引入了边界框损失函数SIoU(Scylla IoU),在训练过程中提高模型的检测精度,加快模型的收敛速度。实验结果显示IDOU-YOLO模型的精确率、召回率、F_(1)分数、mAP@0.5和mAP@0.5:0.95分别达到99.2%、96.3%、97.7%、98.4%和70.2%,表明IDOU-YOLO算法模型在红外无人机目标检测任务中具有显著优势和应用潜力。

Low-altitude small unmanned aerial vehicle(UAV)frequently invade sensitive areas,which posing a serious threat to national and social security.Aiming at the problems such as high missed detection rate and insufficient detection accuracy for UAV target detection based on thermal imaging,the infrared detection of UAV-YOLO(IDOU-YOLO)algorithm model was proposed.A cross-scale fusion feature pyramid mechanism was constructed to fully explore the feature space information,focus on the cross-scale information fusion and the rich information representation ability of the model,and enhance the target recognition ability.At the same time,the bounding box loss function Scylla IoU(SIoU)was introduced to improve the detection accuracy and accelerate the convergence speed of the model in the training process.The experimental results show that the precision,recall,F_(1)score,mAP@0.5 and mAP@0.5:0.95 reach 99.2%,96.3%,97.7%,98.4%and 70.2%,respectively,which indicates that the IDOU-YOLO model improves the detection and recognition ability of UAV targets in various scenarios,and can better meet the application requirements of anti-UAV systems.