针对红外弱小 目标像元数量少、图像背景复杂、检测精度低且耗时较长的问题,文中提出了一种多深度特征连接的红外弱小 目标检测模型(MFCNet).首先,提出了多深度交叉连接主干形式以增加不同层间的特征传递,增强特征提取能力;其次,设计了注意力引导的金字塔结构对深层特征进行目标增强,分离背景与 目标;提出非对称融合解码结构加强解码中纹理信息与位置信息保留;最后,引入点回归损失得到中心坐标.所提网络模型在SIRST公开数据集与 自建长波红外弱小目标数据集上进行训练并测试,实验结果表明,与现有数据驱动和模型驱动算法相比,所提算法在复杂场景下具有更高的检测精度及更快的速度,模型的平均精度相比次优模型提升了 5.41%,检测速度达到100.8FPS.
Small infrared targets have the characteristics of a small number of pixels and a complex background,which leads to the problems of low detection accuracy and high time-consumption.This paper proposes a multi-depth feature connection net-work.Firstly,the model proposes a multi-depth cross-connect backbone to increase feature transfer between different layers and enhance feature extraction capabilities.Secondly,an attention-guided pyramid structure is designed to enhance the deep features and separate the background from the target.Thirdly,an asymmetric fusion decoding structure is proposed to enhance the preser-vation of texture information and position information in decoding.Finally,the model introduces point regression loss to get the center coordinates.The proposed network model is trained and tested on the SIRST dataset and the self-built infrared small target dataset.Experimental results show that compared with existing data-driven and model-driven algorithms,the proposed model has higher detection accuracy and faster speed in complex scenes.Compared with the suboptimal model,the average precision of the model is improved by 5.41%,and the detection speed reaches 100.8 FPS.