重庆理工大学学报2024，Vol.38Issue(9)：55-60,6.DOI:10.3969/j.issn.1674-8425(z).2024.05.007
面向微型交通标志的ASPC-YOLOv8检测算法
ASPC-YOLOv8 detection algorithm for miniature traffic signs
摘要
Abstract
To address the misdetection and omission of miniature traffic signs under partial occlusion and complex background, this paper proposes a traffic sign detection framework based on YOLOv8s.First, the adaptive spatial pyramid convolution module ( ASPC) is built to replace the Neck part of the Conv module.A new ASPC2f module is designed to replace the part of the C2f module, reducing the number of model parameters and improving the detection performance.To mitigate the semantic information loss due to the inconsistency of scale in detecting the miniature targets, a miniature target detection layer is introduced to enhance the effective fusion of deep and shallow semantic information.Finally, the EIOU is employed to replace original bounding box loss function to improve the network bounding box regression performance.Our experimental results show the method achieves 89.7％ mean average precision (mAP) on the TT100K traffic sign dataset, 6.2 percentage points higher compared to that of the original model.Meanwhile, it is 9 .4 percentage points higher in the mean average precision of the microtargets and reduces the number of parameters by 2.6 MB.关键词
交通标志检测/小目标检测/空间金字塔卷积/特征融合Key words
traffic sign detection/small target detection/spatial pyramid convolution/feature fusion分类
交通工程引用本文复制引用
陈其彬,邓涛,杨志军,汪世豪,李彦波,韩振宇,陈梓山..面向微型交通标志的ASPC-YOLOv8检测算法[J].重庆理工大学学报,2024,38(9):55-60,6.基金项目
国家自然科学基金项目(52275051) （52275051）
重庆市教育委员会科学技术研究重点项目(KJZD-K202000701) （KJZD-K202000701）
重庆交通大学自然科学类揭榜挂帅项目(XJ2023000701) （XJ2023000701）
重庆市自然科学基金项目(CSTB2022NSCQ-LZX0068) （CSTB2022NSCQ-LZX0068）
