湖北民族大学学报（自然科学版）2025，Vol.43Issue(2)：202-209,8.DOI:10.13501/j.cnki.42-1908/n.2025.06.010
基于IEMA和混合小目标的YOLOv8s交通标志检测方法
The YOLOv8s Traffic Sign Detection Method Based on IEMA and Mixed Small Targets
摘要
Abstract
Aiming at the problem of low precision in traffic sign detection in complex scenarios,a improved efficient multi-scale attention mechanism and hybrid small target layer-you only look once version 8 small model(AMST-YOLOv8s)was proposed.Firstly,the improved efficient multi-scale attention(IEMA)module was added before the spatial pyramid pooling-fast(SPPF)module in the backbone network to improve the detection precision of traffic signs.Secondly,a hybrid small target detection layer was added to improve the detection precision of small objects in traffic sign detection.Finally,by improving the shape-intersection over union(Shape-IOU),the wise shape-intersection over union(WSIOU)loss function was obtained to replace the complete intersection over union(CIOU)loss function in the model,enhancing the model's ability to detect the diverse shapes of the objects.On the Tsinghua-Tencent 100k(TT100K)dataset,the precision of the AMST-YOLOv8s model was 5.33%higher,the recall rate was 11.30%higher,the mean average precision(mAP)at an intersection over union threshold of 0.5 was 7.78%higher,and the mean average precision(mAP)at an intersection over union threshold from 0.50 to 0.95 was 6.67%higher than those of the original YOLOv8s model.On the Changsha University of Science and Technology-Chinese traffic sign detection benchmark(CCTSDB),stock keeping unit 100k(SKU-100K),visual drones(VisDrone),and visual object classes 2007(VOC2007)datasets,the mAP at an intersection over union threshold of 0.5 of the AMST-YOLOv8s model was 37.10%,0.50%,10.70%,and 1.17%higher respectively than that of the original YOLOv8s model.The results showed that compared with the mainstream traffic sign detection models,the proposed model had the advantages of high precision,strong generalization ability,and light weight.关键词
交通标志检测/IEMA机制/混合小目标层/小目标检测/YOLOv8/WSIOUKey words
traffic sign detection/improved efficient multi-scale attention mechanism/hybrid small target layer/small object detection/YOLOv8/wise shape intersection over union分类
计算机与自动化引用本文复制引用
朱强军,胡斌..基于IEMA和混合小目标的YOLOv8s交通标志检测方法[J].湖北民族大学学报（自然科学版）,2025,43(2):202-209,8.基金项目
安徽师范大学皖江学院重点自然科学研究项目(WJKYZD-202301) （WJKYZD-202301）
安徽省高等学校省级质量工程项目(2022sx052) （2022sx052）
安徽省高校自然科学研究重点项目(2023AH052459). （2023AH052459）
