西华大学学报（自然科学版）2025，Vol.44Issue(3)：29-36,8.DOI:10.12198/j.issn.1673-159X.5422
基于改进YOLOv8的低光照行人检测算法
Low Light Pedestrian Detection Algorithm Based on Improved YOLOv8
摘要
Abstract
Pedestrian detection is widely used in the fields of intelligent transportation and autonom-ous driving.However,in low light scenarios,pedestrian detection has problems such as missed detection and false detection,which result in reduced detection accuracy.Therefore,a low light pedestrian detection algorithm GSG-YOLOv8 based on YOLOv8 improvement is proposed.Firstly,the GCNet module is ad-ded to the backbone network to enhance the model's ability to extract contextual information from images.Then,SPDConv and Conv are integrated into the backbone network to enhance the model's ability to ex-tract local features and improve the effectiveness of small object detection.Finally,GAM attention mechan-ism is added to the neck network to adaptively adjust the correlation between the target and background,and reduce the interference of background information.Compared to the baseline YOLOv8n algorithm,the improved algorithm performs mAP@0.5 And mAP@0.5～0.95 increased by 3.4 and 4.6 respectively.Com-pared to other mainstream algorithms,the improved algorithm has lower system overhead and higher ob-ject detection accuracy.The experimental results show that the GSG-YOLOv8 algorithm overcomes the in-fluence of low light and improves the accuracy of pedestrian detection under low light conditions.关键词
行人检测/YOLOv8/低光照/特征提取/小目标检测/注意力机制Key words
pedestrian detection/YOLOv8/low-light/feature extraction/small target detection/attention mechanism分类
信息技术与安全科学引用本文复制引用
李宝兵,符长友..基于改进YOLOv8的低光照行人检测算法[J].西华大学学报（自然科学版）,2025,44(3):29-36,8.基金项目
企业信息化与物联网测控技术四川省高校重点实验室项目(2021WYY02). （2021WYY02）
