YOLOv10-Based Real-Time Pedestrian Detection for Autonomous Vehicles

被引：0

作者：

Li, Yan ^{[1
,2
]}

Leong, Waiyie ^{[3
]}

Zhang, Hongli ^{[1
,2
]}

机构：

[1] INTI Int Univ, Nilai, Negeri Sembilan, Malaysia

[2] Heilongjiang Inst Construct Technol, Harbin, Peoples R China

[3] INTI Int Univ, Fac Engn & Quant Surveying, Nilai, Negeri Sembilan, Malaysia

来源：

2024 IEEE 8TH INTERNATIONAL CONFERENCE ON SIGNAL AND IMAGE PROCESSING APPLICATIONS, ICSIPA | 2024年

关键词：

YOLOv10; Real-Time Pedestrian Detection; Autonomous Driving; EfficientNet; BiFormer;

D O I：

10.1109/ICSIPA62061.2024.10686546

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Accurate pedestrian detection is increasingly important for safety as autonomous driving technology advances. This paper presents a real-time pedestrian detection method based on YOLOv10. The technique creates an efficient real-time object detection model by enhancing the backbone network with EfficientNet and C2F-DM modules, integrating the BiFormer module in the neck network, and incorporating a multi-scale feature fusion detection head. Experimental results show that YOLOv10 can achieve efficient multi-scale pedestrian detection, even in complex backgrounds.

引用

页数：6

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