计算机与数字工程2024，Vol.52Issue(1)：145-149,155,6.DOI:10.3969/j.issn.1672-9722.2024.01.023
基于多尺度特征融合的YOLOv3行人检测算法
YOLOv3 Pedestrian Detection Algorithm Based on Multi-scale Feature Fusion
摘要
Abstract
With popularization and promotion of deep learning techniques in the field of computer,the pedestrian detection technology has been further improved,but still on several occasions there is a big problem,for example the pedestrian size differ-ence,dense pedestrian detection,in the above two cases,the pedestrian detection performance fell sharply,there exist residual sit-uation and false detection.For pedestrians size problem,YOLOv3 algorithm is introduced in the feature extraction of network multi-scale feature fusion module,changing the original multiple convolution of residual layer stack unit,increasing network depth of feature extraction and improving the network of the different scales of pedestrian feature extraction ability,so as to improve the pe-destrian detection accuracy and robustness of the algorithm.Experimental results show that the average accuracy of the improved al-gorithm is 5.49%and 2.26%higher than that of the benchmark algorithm after training in Caltech and ON_MERGE data sets.关键词
多尺度特征融合/YOLOv3算法/行人大小尺度/行人检测Key words
multi-scale feature fusion/YOLOv3 algorithm/pedestrian size scale/pedestrian detection分类
信息技术与安全科学引用本文复制引用
黎国斌,王等准,张剑,扈健玮,林向会,谢本亮..基于多尺度特征融合的YOLOv3行人检测算法[J].计算机与数字工程,2024,52(1):145-149,155,6.基金项目
国家自然科学基金项目(编号:61562009) （编号:61562009）
贵州大学引进人才科研项目(编号:贵大人基合字(2015)29号) （编号:贵大人基合字(2015）
半导体功率器件教育部工程研究中心开放基金项目(编号:ERCMEKFJJ2019-(06))资助. （编号:ERCMEKFJJ2019-(06）
