在低亮度条件下,传统的事故勘察方法难以获取高质量的勘察数据。提出了基于机载激光雷达的低照度事故现场重建方法。首先,建立机载激光雷达勘察的方法框架。接着,使用高斯分布的统计学滤波算法去除噪点,通过判断空间划分体素的占据状态...在低亮度条件下,传统的事故勘察方法难以获取高质量的勘察数据。提出了基于机载激光雷达的低照度事故现场重建方法。首先,建立机载激光雷达勘察的方法框架。接着,使用高斯分布的统计学滤波算法去除噪点,通过判断空间划分体素的占据状态来滤除现场周围移动物体。然后,利用传感器自身的位姿数据配准点云数据,建立事故现场的三维点云模型。此外,探究了无人机飞行高度和激光旁向重叠率如何影响建模精度。最后,在夜间模拟事故现场进行实证研究,研究发现当无人机飞行高度为15 m,激光旁向重叠率为50%时,建模精度和处理时间能达到较好平衡。与航拍摄影建模、传统人工勘察方法相比,机载激光雷达建模均方根误差(root mean square error,RMSE)为0.04636,低于航拍摄影建模误差,表明方法能够应用于低照度交通事故现场勘测。展开更多
This research focuses on addressing the challenges associated with image detection in low-light environments,particularly by applying artificial intelligence techniques to machine vision and object recognition systems...This research focuses on addressing the challenges associated with image detection in low-light environments,particularly by applying artificial intelligence techniques to machine vision and object recognition systems.The primary goal is to tackle issues related to recognizing objects with low brightness levels.In this study,the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information images.The detection scenarios are categorized into normal brightness and low brightness situations.When the system determines a normal brightness environment,normal brightness images are recognized using deep learning methods.In low-brightness situations,three methods are proposed for recognition.The first method is the SegmentationwithDepth image(SD)methodwhich involves segmenting the depth image,creating amask from the segmented depth image,mapping the obtained mask onto the true color(RGB)image to obtain a backgroundreduced RGB image,and recognizing the segmented image.The second method is theHDVmethod(hue,depth,value)which combines RGB images converted to HSV images(hue,saturation,value)with depth images D to form HDV images for recognition.The third method is the HSD(hue,saturation,depth)method which similarly combines RGB images converted to HSV images with depth images D to form HSD images for recognition.In experimental results,in normal brightness environments,the average recognition rate obtained using image recognition methods is 91%.For low-brightness environments,using the SD method with original images for training and segmented images for recognition achieves an average recognition rate of over 82%.TheHDVmethod achieves an average recognition rate of over 70%,while the HSD method achieves an average recognition rate of over 84%.The HSD method allows for a quick and convenient low-light object recognition system.This research outcome can be applied to nighttime surveillance systems or nighttime road safety systems.展开更多
针对现有的目标检测算法检测表面亮度低的小尺度星系时效果不理想的问题,该文提出了一种基于掩码机制与目标交叉认证的低表面亮度的小尺度星系检测方法。首先,针对天文图像设计了一个基于目标总数变化率的阈值确定方法来获取阈值;其次,...针对现有的目标检测算法检测表面亮度低的小尺度星系时效果不理想的问题,该文提出了一种基于掩码机制与目标交叉认证的低表面亮度的小尺度星系检测方法。首先,针对天文图像设计了一个基于目标总数变化率的阈值确定方法来获取阈值;其次,设计了基于掩码机制的目标去除方法和基于自适应半径的点源区域获取方法,结合图像分割和点源检测算法生成非检测目标掩码,与原图进行逐点相乘去除图中体积较大、亮度较高的非检测目标,得到亮度微弱、体积较小的候选者;然后,利用图像分割技术获取候选体分割区域,计算区域面积和质心坐标定位候选者;最后,通过目标交叉认证的方法将候选者与星表中真实记录的星体进行坐标差值计算获取星系目标。实验与分析表明,在SDSS(Sloan Digital Sky Survey)天文数据集上该方法对低表面亮度的小尺度目标检测率可达约94.90%,星系的识别率可达到约89.21%,都高于经典的目标检测算法。展开更多
文摘在低亮度条件下,传统的事故勘察方法难以获取高质量的勘察数据。提出了基于机载激光雷达的低照度事故现场重建方法。首先,建立机载激光雷达勘察的方法框架。接着,使用高斯分布的统计学滤波算法去除噪点,通过判断空间划分体素的占据状态来滤除现场周围移动物体。然后,利用传感器自身的位姿数据配准点云数据,建立事故现场的三维点云模型。此外,探究了无人机飞行高度和激光旁向重叠率如何影响建模精度。最后,在夜间模拟事故现场进行实证研究,研究发现当无人机飞行高度为15 m,激光旁向重叠率为50%时,建模精度和处理时间能达到较好平衡。与航拍摄影建模、传统人工勘察方法相比,机载激光雷达建模均方根误差(root mean square error,RMSE)为0.04636,低于航拍摄影建模误差,表明方法能够应用于低照度交通事故现场勘测。
基金the National Science and Technology Council of Taiwan under Grant NSTC 112-2221-E-130-005.
文摘This research focuses on addressing the challenges associated with image detection in low-light environments,particularly by applying artificial intelligence techniques to machine vision and object recognition systems.The primary goal is to tackle issues related to recognizing objects with low brightness levels.In this study,the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information images.The detection scenarios are categorized into normal brightness and low brightness situations.When the system determines a normal brightness environment,normal brightness images are recognized using deep learning methods.In low-brightness situations,three methods are proposed for recognition.The first method is the SegmentationwithDepth image(SD)methodwhich involves segmenting the depth image,creating amask from the segmented depth image,mapping the obtained mask onto the true color(RGB)image to obtain a backgroundreduced RGB image,and recognizing the segmented image.The second method is theHDVmethod(hue,depth,value)which combines RGB images converted to HSV images(hue,saturation,value)with depth images D to form HDV images for recognition.The third method is the HSD(hue,saturation,depth)method which similarly combines RGB images converted to HSV images with depth images D to form HSD images for recognition.In experimental results,in normal brightness environments,the average recognition rate obtained using image recognition methods is 91%.For low-brightness environments,using the SD method with original images for training and segmented images for recognition achieves an average recognition rate of over 82%.TheHDVmethod achieves an average recognition rate of over 70%,while the HSD method achieves an average recognition rate of over 84%.The HSD method allows for a quick and convenient low-light object recognition system.This research outcome can be applied to nighttime surveillance systems or nighttime road safety systems.
文摘针对现有的目标检测算法检测表面亮度低的小尺度星系时效果不理想的问题,该文提出了一种基于掩码机制与目标交叉认证的低表面亮度的小尺度星系检测方法。首先,针对天文图像设计了一个基于目标总数变化率的阈值确定方法来获取阈值;其次,设计了基于掩码机制的目标去除方法和基于自适应半径的点源区域获取方法,结合图像分割和点源检测算法生成非检测目标掩码,与原图进行逐点相乘去除图中体积较大、亮度较高的非检测目标,得到亮度微弱、体积较小的候选者;然后,利用图像分割技术获取候选体分割区域,计算区域面积和质心坐标定位候选者;最后,通过目标交叉认证的方法将候选者与星表中真实记录的星体进行坐标差值计算获取星系目标。实验与分析表明,在SDSS(Sloan Digital Sky Survey)天文数据集上该方法对低表面亮度的小尺度目标检测率可达约94.90%,星系的识别率可达到约89.21%,都高于经典的目标检测算法。
