摘要
针对红外行人图像中待检测目标存在多尺度及部分遮挡导致传统算法难以准确检测的问题,提出一种动态特征优化机制下的跨尺度红外行人检测算法。为解决复杂环境中行人目标特征难以有效表达进而造成目标检测精度低的问题,提出一种动态特征优化机制,通过设计亮度感知模块及EG-Chimp优化模型在增强输入图像局部对比度的同时抑制背景信息;搭建了CSPDarkNet特征提取网络,并在其基础上构建CSFF-BiFPN特征金字塔结构以及跨尺度特征融合模块,以提高检测网络对多尺度及部分遮挡行人目标的检测精度;为进一步精确定位行人目标,引入CIOU损失函数加速网络收敛,从而提升检测性能。选取9种经典检测算法在KAIST数据集上进行对比测试,实验结果表明,本文算法能够对复杂环境中的多尺度及部分遮挡红外行人目标进行准确检测,检测精度可达90.7%,验证了所提出检测网络的优势。
Multi-scale and partial occlusions in infrared pedestrian images for target detection make it difficult for traditional algorithms to achieve accurate detection.This study developed a cross-scale infrared pedestrian detection algorithm based on a dynamic feature optimization mechanism.First,to alleviate the limitation that pedestrian target features are difficult to express effectively in complex environments,which results in low target detection accuracy,a dynamic feature optimization mechanism is presented.The luminance perception module and EG-Chimp optimization model are designed to enhance the local contrast of the input image and suppress background information.Second,the CSPdarknet53 structure is utilized as the backbone feature extraction network.Accordingly,a CSFF-BiFPN feature pyramid structure and cross-scale feature fusion module are constructed to improve the detection accuracy of multi-scale and partially occluded pedestrian targets.Finally,the CIOU loss function is introduced to accelerate network convergence rate and improve detection performance to locate pedestrian targets more accurately.To verify the advantages of the proposed detection network,nine classical detection algorithms are selected as baseline methods and tested on KAIST datasets.Experimental results demonstrate that the proposed algorithm can accurately detect multi-scale and partially occluded infrared pedestrian targets in complex environments,with detection accuracies of up to 90.7%.
作者
郝帅
何田
马旭
杨磊
孙思雅
HAO Shuai;HE Tian;MA Xu;YANG Lei;SUN Siya(College of Electrical and Control Engineering,Xi'an University of Science and Technology,Xi'an 710054,China)
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2022年第19期2390-2403,共14页
Optics and Precision Engineering
基金
国家自然科学基金资助项目(No.51804250)
中国博士后科学基金资助项目(No.2019M653874XB,No.2020M683522)
陕西省科技计划项目(No.2021JQ-572,No.2020JQ-757)
陕西省教育厅科研计划项目(No.18JK0512,No.21JK0769)
西安市碑林区科技计划项目(No.GX2116)。
关键词
红外行人检测
跨尺度
动态特征优化
亮度感知
特征融合
infrared pedestrian detection
cross scale
dynamic feature optimization
luminance perception
feature fusion
