摘要
红外图像因为存在噪声大、对比度不佳等问题,容易导致目标检测时的精度降低,本文结合YOLOX和Swin Transformer,提出了一种改进的YOLOX的模型。改进的模型采用Swin Transformer替换YOLOX中的CSPDarknet主干提取网络,减少YOLOX中Neck和Head部分的激活函数以及标准化层,以提高特征的提取能力,优化网络结构。对改进的模型在艾瑞光电数据集和FILR数据集上均进行了测试,实验结果显示,改进后的YOLOX网络,在两个数据集上的平均检测精度都有明显提升,更加适合红外图像的目标检测。
Owing to the problems of high noise and poor contrast in infrared images,the accuracy of target detection is easily reduced.Here,an improved YOLOX model combined with YOLOX and a Swin Transformer is proposed.To improve the feature extraction ability,reduce the activation functions and standardization layers of the neck and head parts in YOLOX,and optimize the network structure,the Swin Transformer is used to replace the CSPDarknet backbone extraction network in YOLOX.This study tests the improved model on both the InfiRay and FILR datasets.The obtained experimental results indicate that the improved YOLOX network has significantly improved the average detection accuracy on both datasets and is more suitable for infrared image target detection.
作者
楼哲航
罗素云
LOU Zhehang;LUO Suyun(School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)
出处
《红外技术》
CSCD
北大核心
2022年第11期1167-1175,共9页
Infrared Technology
