基于改进YOLOv8n的减速带检测算法研究  

Research on Speed Bump Detection Algorithm Based on Improved YOLOv8n

作　　者：李子文 马荣[1] LI Ziwen;MA Rong(School of Mechanics and Transportation,Southwest Forestry University,Kunming 650224,China)

机构地区：[1]西南林业大学机械与交通学院,云南昆明650224

出　　处：《汽车实用技术》2025年第8期45-49,76,共6页Automobile Applied Technology

摘　　要：针对汽车自动驾驶在障碍物目标检测缺少对汽车自动驾驶舒适性,且减速带作为日常生活中常见的交通设施会影响到车辆的舒适性的问题,文章基于YOLOv8n改进算法实现在良好光线条件下减速带检测。首先,构建在光线良好条件下不同距离和不同环境下的减速带数据集;其次,在YOLOv8n的基础上添加SimAM注意力机制,提高网络对特征图的感知;最后,将原网络损失函数CIoU替换为SIoU,提高网络收敛速度及鲁棒性。结果表明,改进后的模型的均值平均精度mAP@0.5提高4.3%,通过实验小车在不同距离下对减速带的检测进行验证,成功验证了改进模型的有效性,检测方案能准确提供减速带检测的预前信息。In view of the lack of comfort for vehicle autonomous driving in obstacle target detection,and the problem that speed bumps,as a common traffic facility in daily life,will affect vehicle comfort,this paper realizes speed bump detection under good light conditions based on YOLOv8n improved algorithm.First,the speed bump data set is constructed at different distances and in different environments under good lighting conditions.Secondly,the SimAM attention mechanism is added on the basis of YOLOv8n to improve the network's perception of feature maps.Finally,the original loss function CIoU is replaced by SIoU to improve the convergence speed and robustness of the network.The results show that the mean average accuracy of the improved model mAP@0.5 is increased by 4.3%.The effectiveness of the improved model is successfully verified through the detection of deceleration belts by experimental vehicles at different distances,and the detection scheme can accurately provide the pre-detection information of deceleration belts.

关 键 词：YOLOv8n 减速带检测 SimAM SIoU 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] U461.99[自动化与计算机技术—计算机科学与技术]