基于改进YOLOv7-Tiny的高速公路入口两轮车辆闯入检测
Two wheeled vehicle intrusion detection at highway entrance based on improved YOLOv7-Tiny
王宏 1田恬1
作者信息
- 1. 西安石油大学计算机学院,西安 710065
- 折叠
摘要
近年来,浙江、福建等省区相继出台相关地方性法规,禁止两轮车辆(摩托车、电动车等)通行高速公路.针对高速公路入口工作人员难以实时检测到两轮车辆闯入的问题,提出一种改进YOLOv7-Tiny的两轮车辆闯入检测算法.首先,从VOC2005中提取摩托车图片并增补了带有入口背景的图片后形成新数据集;其次基于YOLOv7-tiny,引入ECA注意力机制,使模型更加聚焦训练摩托车相关目标特征.使用ssFPN网络,对小目标特征信息进行增强;采用基于动态非单调机制的WIoU损失函数,提高对于小物体检测的准确性;使用Adam优化器,提升回归过程的收敛速度和准确性.改进后的算法,mAP、Precision、Recall分别提高了2.63、4.01、13.92个百分点,F1提高0.10,表明该方法具有显著的有效性.
Abstract
In recent years,provinces such as Zhejiang and Fujian have successively introduced relevant local regulations pro-hibiting two wheeled vehicles(such as motorcycles,electric vehicles,etc.)from passing through highways.A modified YOLOv7-Tiny two wheeled vehicle intrusion detection algorithm is proposed to address the issue of real-time detection of two wheeled vehicle in-trusion by highway entrance workers.Firstly,motorcycle images were extracted from VOC2005 and images with entrance back-grounds were added to form a new dataset.Secondly,based on YOLOv7-Tiny,an ECA attention mechanism was introduced to make the model more focused on training motorcycle related target features.The ssFPN network was used to enhance small target feature information,and a WIoU loss function based on dynamic non monotonic mechanism was used to improve the accuracy of small ob-ject detection.Finally,use the Adam optimizer to improve the convergence speed and accuracy of the regression process.The im-proved algorithm improves mAP,Precision,Recall by 2.63,4.01 and 13.92 percentage point,respectively,and improves F1 by 0.10,indicating significant effectiveness of the method.
关键词
两轮车辆闯入检测/YOLOv7-tiny/ECA注意力机制/ssFPN/WIoUKey words
two wheeled vehicle intrusion detection/YOLOv7-Tiny/ECA attention mechanism/ssFPN/WIoU引用本文复制引用
出版年
2024
NETL
NSTL
万方数据