Aiming at the defects of traditional four-wheel aligner such as many sensors,complex operation and slow detection speed,a fast and accurate 3D four-wheel alignment detection method is studied.Firstly,a new and special...Aiming at the defects of traditional four-wheel aligner such as many sensors,complex operation and slow detection speed,a fast and accurate 3D four-wheel alignment detection method is studied.Firstly,a new and special circle center target board is designed to calibrate the camera,and then the registration of the homography matrix is optimized by using the improved RANSAC(Random sample consensus)algorithm combined with the designed special target board,and the parameters of the wheel alignment system are adjusted by using the space vector principle.Accurate measurements are made to obtain the parameters of the four-wheel alignment.Design a calibration comparison experiment between the traditional target board and the new type of target board,and conduct a comparative test with the existing four-wheel aligner of the depot.The experimental results show that the use of the new target board-binding optimization algorithm can improve the calibration efficiency by about 9%to 21%,while improving the calibration accuracy by about 10.6%to 17.8%.And through the real vehicle test,it is verified that the use of the new target combined with the optimization algorithm can ensure the accuracy and reliability of the four-wheel positioning.This method has a certain significance in the rapid detection of vehicle four-wheel alignment parameters.展开更多
钢拱桥的线形监测是桥梁健康监测系统的重要组成部分。运用三维激光扫描技术,融合随机抽样一致(random sample consensus,RANSAC)算法对传统的具有噪声的基于密度的聚类方法(density-based spatial clustering of applications with noi...钢拱桥的线形监测是桥梁健康监测系统的重要组成部分。运用三维激光扫描技术,融合随机抽样一致(random sample consensus,RANSAC)算法对传统的具有噪声的基于密度的聚类方法(density-based spatial clustering of applications with noise,DBSCAN)算法进行改进,对钢拱桥拱肋线形进行提取。三维激光点云数据具有全面性和细节体现的优势,能够完整地呈现桥梁结构的形状和变形信息,融合RANSAC的改进DBSCAN算法根据钢拱桥结构特征对聚类结果进行约束,能够很好地实现删除离散点及桥面、横撑、横联和腹杆部分的点云这一目的。根据融合RANSAC的改进DBSCAN算法提取出的点云进行关键点拟合,与人工提取结果进行对比,拱肋关键点提取误差均在毫米级,最大误差为9.2 mm,最小误差为0.1 mm,此提取方法能够更加准确有效地完成钢拱桥线形提取,使线形提取精度达到毫米级,大大降低了人力成本和时间成本,对钢拱桥的复杂结构有更好的鲁棒性,能很好地适应实际生产需求。展开更多
在复杂梨园环境中,传统视觉导航方法容易受到光照变化、杂草遮挡等因素的干扰。针对此问题,本文提出了一种基于改进YOLO v8模型的梨园导航线提取方法。该方法在YOLO v8模型中集成了多尺度大核注意力(Multi-scale large kernel attention...在复杂梨园环境中,传统视觉导航方法容易受到光照变化、杂草遮挡等因素的干扰。针对此问题,本文提出了一种基于改进YOLO v8模型的梨园导航线提取方法。该方法在YOLO v8模型中集成了多尺度大核注意力(Multi-scale large kernel attention,MLKA)模块以增强对树干特征的感知。设计了多帧特征点融合机制,通过记录并利用连续5帧图像中检测到的特征点,有效弥补了单帧图像特征点不足的问题。此外,引入随机抽样一致性(Random sample consensus,RANSAC)算法,分别对左右两侧树行的特征点进行降噪处理,并使用最小二乘法进行树行线拟合。通过计算左右两侧树行线的角平分线生成果园导航线。实验结果表明:改进模型在复杂的果园环境中,树干检测的精确率(Precision)达到89.8%,召回率(Recall)达到79.9%,平均精度均值(mean average precision,mAP50-95)达到了55.1%。结合多帧特征点融合与RANSAC降噪生成的导航线与手动标注的参考导航线之间的角度偏差均值为1.17°,位置偏差均值为20.40像素,均方根偏差均值为0.27。本文方法为梨园环境中的视觉导航提供了一种低成本、高适应性的技术方案。展开更多
基金Anhui Province Key Research and Development Program(No.2022107020012)Shenzhen Science and Technology Innovation Project(No.JSGG20191129102008260)。
文摘Aiming at the defects of traditional four-wheel aligner such as many sensors,complex operation and slow detection speed,a fast and accurate 3D four-wheel alignment detection method is studied.Firstly,a new and special circle center target board is designed to calibrate the camera,and then the registration of the homography matrix is optimized by using the improved RANSAC(Random sample consensus)algorithm combined with the designed special target board,and the parameters of the wheel alignment system are adjusted by using the space vector principle.Accurate measurements are made to obtain the parameters of the four-wheel alignment.Design a calibration comparison experiment between the traditional target board and the new type of target board,and conduct a comparative test with the existing four-wheel aligner of the depot.The experimental results show that the use of the new target board-binding optimization algorithm can improve the calibration efficiency by about 9%to 21%,while improving the calibration accuracy by about 10.6%to 17.8%.And through the real vehicle test,it is verified that the use of the new target combined with the optimization algorithm can ensure the accuracy and reliability of the four-wheel positioning.This method has a certain significance in the rapid detection of vehicle four-wheel alignment parameters.
文摘在复杂梨园环境中,传统视觉导航方法容易受到光照变化、杂草遮挡等因素的干扰。针对此问题,本文提出了一种基于改进YOLO v8模型的梨园导航线提取方法。该方法在YOLO v8模型中集成了多尺度大核注意力(Multi-scale large kernel attention,MLKA)模块以增强对树干特征的感知。设计了多帧特征点融合机制,通过记录并利用连续5帧图像中检测到的特征点,有效弥补了单帧图像特征点不足的问题。此外,引入随机抽样一致性(Random sample consensus,RANSAC)算法,分别对左右两侧树行的特征点进行降噪处理,并使用最小二乘法进行树行线拟合。通过计算左右两侧树行线的角平分线生成果园导航线。实验结果表明:改进模型在复杂的果园环境中,树干检测的精确率(Precision)达到89.8%,召回率(Recall)达到79.9%,平均精度均值(mean average precision,mAP50-95)达到了55.1%。结合多帧特征点融合与RANSAC降噪生成的导航线与手动标注的参考导航线之间的角度偏差均值为1.17°,位置偏差均值为20.40像素,均方根偏差均值为0.27。本文方法为梨园环境中的视觉导航提供了一种低成本、高适应性的技术方案。
