电讯技术2026，Vol.66Issue(2)：239-246,8.DOI:10.20079/j.issn.1001-893x.240905002
RHL-YOLOv8:一种轻量级的交通车辆检测算法
RHL-YOLOv8:a Lightweight Traffic Vehicle Detection Algorithm
摘要
Abstract
To address the challenges of high parameter counts,substantial computational demands,and deployment difficulties associated with deep learning object detection methods in real-world traffic scenarios,a YOLOv8n-based lightweight vehicle detection algorithm named RHL-YOLOv8 is proposed.Firstly,a novel C2f-RVB module is introduced within the feature extraction network by leveraging the RepViTBlock module(RVB).This eliminates the computational and memory overhead associated with skip connections while preserving critical feature information,thereby reducing the overall network parameters.Secondly,the hybrid structure feature pyramid network(HSFPN)is enhanced by using an efficient local attention(ELA)mechanism to mitigate the loss of small-scale feature information.Finally,a new lightweight shared convolutional detection head(LSCD)incorporating group normalization(GN)convolution and scale layers is designed.This compensates for any accuracy loss due to feature fusion while maintaining a lightweight architecture.Experimental results demonstrate that the proposed algorithm achieves a 1.4%improvement in mAP@0.5 compared to the YOLOv8 algorithm on the UA-DETRAC dataset,reduces the number of parameters by 50.0%,decreases computational costs by 32.0%,and attains a frame rate of 99.7 frame per second.On the KITTI dataset,it maintains a stable mAP@0.5 while reducing the parameter count and computational cost by 43.3%and 28.4%,respectively,achieving a balanced trade-off between detection accuracy and model efficiency.关键词
车辆检测/深度学习/YOLOv8nKey words
vehicle detection/deep learning/YOLOv8n分类
信息技术与安全科学引用本文复制引用
彭杰,苏盈盈,杜谦,刘灿,李文杰..RHL-YOLOv8:一种轻量级的交通车辆检测算法[J].电讯技术,2026,66(2):239-246,8.基金项目
重庆市科委科学技术研究项目(CSTB2022NSCQ-MSX1425) （CSTB2022NSCQ-MSX1425）
重庆市教委科学技术研究项目(KJQN202101510) （KJQN202101510）
重庆科技大学研究生创新项目(YKJCX2320403) （YKJCX2320403）
