融合多异构滤波器的轻型弱小目标检测网络  

Light Dim Small Target Detection Network with Multi-Heterogeneous Filters

作　　者：赵菲[1] 邓英捷 Zhao Fei;Deng Yingjie(National Key Laboratory of Science and Technology on ATR,College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,Hunan,China)

机构地区：[1]国防科技大学电子科学学院ATR国防科技重点实验室,湖南长沙410073

出　　处：《光学学报》2023年第9期145-156,共12页Acta Optica Sinica

基　　金：国家自然科学基金青年基金(61901489)。

摘　　要：针对红外图像信息维度单一且弱小目标因特征不明显而难以检测的问题,将不同结构的多滤波器融入YOLOv5n网络,根据增强弱小目标和抑制背景干扰的不同特性分别选择三个异构滤波器作用于网络的多通道输入图像,从而丰富原始图像的信息维度,有效提升后端网络对复杂背景下弱小目标的适应能力;通过添加注意力模块、采用小锚框策略、裁剪网络深层分支等改进措施,在增强YOLOv5n网络弱小目标检测能力的同时,进一步减少了计算和存储资源需求。实验结果表明,所提出的算法能够有效检测红外复杂背景中的弱小目标,同时占用存储和计算资源更少,为算法部署在资源受限的嵌入式设备上提供了基础。Objective Dim small target detection in infrared images with complex backgrounds is a key technology for precise guidance systems and infrared surveillance systems,and the detection performance directly determines the success or failure of tasks.As a result,it has become a hot topic,and different detection methods have been presented.Compared with traditional algorithms,deep network algorithms have achieved remarkable results in many aspects in recent years,and some frameworks designed based on existing deep networks have been applied to detect the dim small target.Although these methods can improve the detection performance of small targets by modifying the network structure because the infrared images have only information of one dimension and limited features in small targets,it is difficult to obtain satisfactory results when the deep network is directly applied to detect dim small targets in the complex infrared background,and the large network scale makes it difficult to deploy the above methods on the embedded platform with constrained resources.Methods In view of the characteristics of single information dimension in infrared images and inconspicuous features of dim small targets,this study enriches the information of original images and incorporates multiple filters with different structures into the YOLOv5n network.In this study,three filters with different structures,namely the Top Hat filter,difference of Gaussian filter(DoG),and mean filter,are selected from the perspective of highlighting targets,suppressing backgrounds,and filtering high-frequency noises.By introducing three heterogeneous filters to process the images in the input layer of the network,the one-dimensional gray information of the original image is expanded into three dimensions,and then they are fed to the network through three channels,which improves the adaptability of the network to dim small targets in complex backgrounds.YOLOV5n network is selected in this study and improved as follows.1)In order to make the deep network improve

关 键 词：机器视觉 红外弱小目标 资源受限 多异构滤波器 YOLOv5n 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]