基于YOLOv7和图像分块的车道线破损检测算法
Lane line damage detection algorithm based on YOLOv7 and image block
温王鹏 1罗文婷 2李林 2张德津 3陈文婷 1吴镇涛1
作者信息
- 1. 福建农林大学交通与土木工程学院,福建福州350000
- 2. 南京工业大学交通运输工程学院,江苏南京211816
- 3. 深圳大学建筑与城市规划学院,广东深圳518060
- 折叠
摘要
提出了一种结合YOLOv7和图像分块的车道线破损检测方法.首先,利用YOLOv7模型检测并提取车道线区域.其次,运用Otsu法计算每个子块的阈值及子块背景区域和目标区域的灰度均值差值,以此实现二值化.然后,采用双线性插值法平滑图像,实现车道线分割,并利用拓扑结构分析法提取车道线轮廓.最后,设计了像素统计、直线拟合、割断检测3种方法判断车道线是否破损.实验结果表明:在不同场景下,该算法在破损车道线检测中的精确率为91.79％,具有较好的检测效果和一定的应用价值.
Abstract
A lane line damage detection method combining YOLOv7 and image block is proposed.Firstly,YOLOv7 model is used to detect and extract the lane line area.Secondly,Otsu algorithm is used to calculate the threshold of each sub block and the gray mean difference between the background area and the target area in the sub block.The binarization is realized accordingly.Then,bilinear interpolation method is used to smooth the image and realize lane line segmentation,and the topological structure analysis method is used to extract the lane line contour.Finally,three methods including pixel statistics,straight line fitting and cutting detection are designed to judge whether the lane line is damaged.Experimental result shows that the precision of the algorithm is 91.79％ in lane line damage detection under different scenarios,which has good detection effect and certain application value.
关键词
车道线破损检测/深度学习/YOLOv7算法/分块分割/最大类间方差法Key words
lane line damage detection/deep learning/YOLOv7 algorithm/block segmentation/Otsu method引用本文复制引用
基金项目
国家重点研发计划资助项目(2021YFB3202901)
福建省高校产学合作重大项目(2020H6009)
出版年
2024
NETL
NSTL
万方数据