首页|基于改进YOLOv5s的航拍红外图像目标识别方法

基于改进YOLOv5s的航拍红外图像目标识别方法

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为了提高无人机在黑暗条件下的识别效率,降低在复杂环境及路况方面存在漏检及延时效果等问题,本文提出了一种改进的 YOLOv5s-GN-CB 红外图像识别方法,该方法可以提高无人机红外航拍图像对车、人等多类目标识别效率.本文对YOLOv5s的主要改进包括以下 3 个方面:将Ghost引入到YOLOv5s主干网络中,并将NWD loss损失函数融入至Ghost中;添加注意力机制CA;添加加权双向特征金字塔BiFPN.经验证,改进的YOLOv5s-GN-CB检测模型在InfiRay红外航拍人车检测数据集下目标识别平均精度均值(mAP@0.5)达到 95.1%,FPS 提高至 75.188 帧/s.相较于YOLOv5原始模型的平均精度均值和FPS分别提高了4.2%和12.02%.在对同一场景中无人机航拍红外图像目标识别的检测精度有明显提升,延时率有所下降.
Aerial Infrared Image Target Recognition Method Based on Improved YOLOv5s
To enhance the recognition efficiency of UAVs in dark conditions and reduce missed detections and delays in complex environments and road conditions,this study proposes an improved YOLOv5s-GN-CB infrared image recognition method.This method enhances the efficiency of UAV infrared aerial images for detecting vehicles,people,and other types of targets.The main improvements to YOLOv5s achieved in this study include the following three aspects:1)introducing the Ghost module into the YOLOv5s backbone network and incorporating NWD loss into Ghost;2)adding the coordinate attention(CA)mechanism;3)incorporating a weighted bidirectional feature pyramid network(BiFPN).The improved YOLOv5s-GN-CB detection model achieves an average accuracy of 95.1%(mAP@0.5)on the InfiRay infrared aerial photography man-vehicle detection dataset,with the FPS increased to 75.188 frames per second.Compared with the original YOLOv5 model,the average accuracy and FPS are improved by 4.2%and 12.02%,respectively.In the same scenario,the detection accuracy of UAV aerial photography infrared image target recognition has been significantly improved,and the delay rate has decreased.

infrared object detectionimproved YOLOv5sghost networkattention mechanism

王悠、韩立祥、付贵

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中国民用航空飞行学院,四川 广汉 618307

四川省通用航空器维修工程技术研究中心,四川 广汉 618307

红外目标检测 改进YOLOv5s Ghost网络 注意力机制

中央高校基本科研业务费基金项目四川省通用航空器维修工程技术研究中心资助课题

J2022-024GAMRC2021ZD01

2024

红外技术
昆明物理研究所 中国兵工学会夜视技术专业委员会

红外技术

CSTPCD北大核心
影响因子:0.914
ISSN:1001-8891
年,卷(期):2024.46(7)