摘要
随着信息化时代的发展,数字技术广泛应用在军事领域。目标检测是武器系统的核心功能,是影响战争局势的重要因素,在侦察、预警及监视等方面具有重要的作用。然而当今目标检测领域主要存在四个问题:小目标检测、小样本检测、检测实时性和遮挡目标检测,小目标检测更是其中的重点和难点。小目标一般只占有几十甚至几个像素,传统检测算法难以依据先验知识,构建适当的特征提取模型并取得精确的检测结果。深度学习检测算法在特征提取时容易丢失特征信息,在复杂多变的应用场景下,容易混淆目标特征与背景噪声。此外,当前的小目标检测算法存在小目标语义特征利用不充分、小目标空间特征不突出等问题。算法检测准确率较低,存在大量漏检和误检现象。针对上述问题,本文提出了一种基于多尺度局部卷积特征关联(Multi-scale Local Convolutional Feature Association,MLCFA)机制的小目标检测算法。MLCFA的核心部分包含局部卷积注意力关联(Local Convolutional Attention Association,LCAA)模块和互注意力特征重构(Cross Attention Feature Reconstruction,CAFR)模块。LCAA模块对特征融合网络得到的多尺度特征图提取特征相关性,并加强小目标内部像素之间的联系,抑制背景噪声的同时突出小目标空间特征的统一性,提高复杂背景下的检测鲁棒性。CAFR模块通过自注意力机制得到100个查询向量,并结合LCAA得到的关联特征序列进行全局特征重构,通过全连接网络得到目标检测信息,一定程度上解决了小目标边界框扰动以及特征缺失的问题。在TinyPerson数据集上的对比实验表明,搭载MLCFA的网络模型与RetinaNet等算法相比,对两类目标检测的F1-Score分别提升了19.81%和11.88%,大幅度提高了小目标检测性能,证明了MLCFA模块的有效性。此外通过收敛速度实验表明,MLAFC只需要50个epoch即可具备良好的检测性能,模型推理较快,具有一定的模型迁移能力。
Digital technology is widely used in the military field owing to the advancement of the information age.Target detection is a core function of the weapon system and an important factor affecting war outcomes,as it is crucial for reconnaissance,early warning,and surveillance.However,target detection has four primary challenges:small target detection,which is the key and most difficult challenge;small sample detection;real-time detection;and occlusion target detection.As small targets generally occupy a few dozen or even a few pixels,constructing appropriate feature extraction models and obtaining accurate detection results based on a priori knowledge is challenging for traditional detection algorithms.Deep learning detection algorithms are prone to losing feature information during feature extraction and easily confuse target features with background noise in complex and changing application scenarios.In addition,the current small target detection algorithms have issues,such as insufficient utilization of small target semantic features,and small target spatial features are not prominent.Consequently,the detection accuracy of these algorithms is low,and several missing and false detection phenomena are observed.This study addresses this issue by proposing a small target detection algorithm based on the multi-scale local convolutional feature association(MLCFA)mechanism.The MLCFA mechanism primarily comprises the local convolutional attention association(LCAA)and cross-attention feature reconstruction(CAFR)modules.The LCAA module extracts the feature association from the multi-scale feature map obtained by the feature fusion network,strengthens the connection between the pixels inside the small target,and highlights the unity of the spatial features of the small target while suppressing background noise to improve detection robustness under a complex background.The CAFR module obtains 100 query vectors via the self-attention mechanism,combines the associated feature sequence obtained by LCAA to carry out global feature reconstruction,and obtains target detection information through the fully connected network,which resolves the issues of small target boundary frame disturbance and missing features to some extent.The comparison experiment on the TinyPerson dataset shows that,compared with RetinaNet and other algorithms,the network model equipped with MLCFA increases the F1 score of the detection of two types of targets by 19.81%and 11.88%,respectively,greatly improving the detection performance of small targets,proving the effectiveness of the MLCFA module.In addition,the convergence rate experiment shows that MLAFC only needs 50 epochs to have good detection performance,indicating that it has fast model inference and some model migration ability.
作者
张梦璇
方榉炫
刘龙
赵秋博
张文博
ZHANG Mengxuan;FANG Juxuan;LIU Long;ZHAO Qiubo;ZHANG Wenbo(School of Artificial Intelligence,Xidian University,Xi’an,Shaanxi 710071,China;School of Electronic Engineering,Xidian University,Xi’an,Shaanxi 710071,China)
出处
《信号处理》
CSCD
北大核心
2024年第11期1990-2006,共17页
Journal of Signal Processing
基金
陕西省技术创新引导计划(2023KXJ-279)。
关键词
小目标检测
多尺度局部卷积特征关联
局部卷积注意力关联
互注意力特征重构
small target detection
multi-scale local convolutional feature association
local convolutional attention association
cross-attention feature reconstruction