摘要
针对当前规模的小目标行人数据集较少,传统行人检测模型对小目标检测效果较差的问题,提出一种基于消隐点性质,提出自适应增殖数据增强和全局上下文特征融合的小目标行人检测方法.利用射影几何与消隐点的性质,对图像中的多个目标进行复制;通过仿射变换投影到新的位置,生成多个大小与背景合理的小目标样本以完成数据增强.利用跨阶段局部网络与轻量化操作改进沙漏结构,融合坐标注意力机制强化骨干网络.设计全局特征融合颈部网络(GFF-neck),以融合全局特征.实验表明,在经过数据增强后的WiderPerson数据集上,改进算法对行人类别的检测AP值达到了79.6%,在VOC数据集上mAP值达到了80.2%.测试结果表明,当搭建实验测试系统进行实景测试时,所提算法有效提升了小目标行人检测识别精度,并满足实时性要求.
A global context feature fusion method for small target pedestrian detection was proposed based on the property of vanishing points and adaptive data augmentation to address the issues of limited small-scale pedestrian datasets and poor detection performance of traditional pedestrian detection models.Multiple targets in the image were copied by using the properties of projective geometry and vanishing points.The targets were projected to new locations through Affine transformation.Multiple small target samples with reasonable size and background were generated to complete data enhancement.The cross stage local network and lightweight operation were used to improve the hourglass structure,and the coordinate attention mechanism was integrated to strengthen the backbone network.The global feature fusion neck network(GFF-neck)was designed to fuse the global features.The experimental results showed that the improved algorithm achieved a detection AP value of 79.6%for pedestrian categories on the data enhanced WiderPerson dataset,and an mAP value of 80.2%on the VOC dataset.An experimental test system was built to test the real scene.The test results show that the proposed algorithm effectively improves the accuracy of small target pedestrian detection and recognition and meets the real-time requirements of the test.
作者
艾青林
杨佳豪
崔景瑞
AI Qing-lin;YANG Jia-hao;CUI Jing-rui(Key Laboratory of Special Purpose Equipment and Advanced Manufacturing Technology,Ministry of Education and Zhejiang Province,Zhejiang University of Technology,Hangzhou 310023,China)
出处
《浙江大学学报（工学版）》
EI
CAS
CSCD
北大核心
2023年第10期1933-1944,1976,共13页
Journal of Zhejiang University：Engineering Science
基金
国家自然科学基金资助项目(52075488)
浙江省自然科学基金资助项目(LY20E050023).
关键词
消隐点
数据增强
全局特征融合
小目标行人检测
轻量化沙漏结构
vanishing point
data enhancement
global feature fusion
small target pedestrian detection
lightweight hourglass structure
