Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Since buildings are inherently elevated objects, these images need to be co-registered with their elevation data for reliable building detection results. However, accurate co-registration is extremely difficult for off-nadir VHR images acquired over dense urban areas. Therefore, this research proposes a Disparity-Based Elevation Co-Registration (DECR) method for generating a Line-of-Sight Digital Surface Model (LoS-DSM) to efficiently achieve image-elevation data co-registration with pixel-level accuracy. Relative to the traditional photogrammetric approach, the RMSE value of the derived elevations is found to be less than 2 pixels. The applicability of the DECR method is demonstrated through elevation-based building detection (EBD) in a challenging dense urban area. The quality of the detection result is found to be more than 90%. Additionally, the detected objects were geo-referenced successfully to their correct ground locations to allow direct integration with other maps. In comparison to the original LoS-DSM development algorithm, the DECR algorithm is more efficient by reducing the calculation steps, preserving the co-registration accuracy, and minimizing the need for elevation normalization in dense urban areas.

Segment-Based Terrain Filtering Technique for Elevation-Based Building Detection in VHR Remote Sensing Images

Building detection in very high resolution (VHR) remote sensing images is crucial for many urban planning and management applications. Since buildings are elevated objects, the incorporation of elevation data provides a mean to reliable detection. However, almost all existing methods of elevation-based building detection must first generate a normalized Digital Surface Model (nDSM). This model is generated by processes of extracting and subtracting terrain elevations from the DSM data. The generation of accurate nDSM is still a challenging task to some extent. This paper introduces a segment-based terrain filtering (SegTF) technique to filter out the terrain elevations directly using DSM elevations. This technique has four steps: elevation co-registration, image segmentation, slope calculation, and building detection. These steps of the developed technique were applied to a dataset that consisted of a VHR image and a corresponding DSM for detecting buildings. The result of the building detection was evaluated and found to be 100% correct with an overall detection quality of 93%. These values indicate a highly reliable and promising technique for mapping buildings in VHR images.

Optical-Elevation Data Co-Registration and Classification-Based Height Normalization for Building Detection in Stereo VHR Images

Building detection in very high resolution (VHR) images is crucial for mapping and analysing urban environments. Since buildings are elevated objects, elevation data need to be integrated with images for reliable detection. This process requires two critical steps: optical-elevation data co-registration and aboveground elevation calculation. These two steps are still challenging to some extent. Therefore, this paper introduces optical-elevation data co-registration and normalization techniques for generating a dataset that facilitates elevation-based building detection. For achieving accurate co-registration, a dense set of stereo-based elevations is generated and co-registered to their relevant image based on their corresponding image locations. To normalize these co-registered elevations, the bare-earth elevations are detected based on classification information of some terrain-level features after achieving the image co-registration. The developed method was executed and validated. After implementation, 80% overall-quality of detection result was achieved with 94% correct detection. Together, the developed techniques successfully facilitate the incorporation of stereo-based elevations for detecting buildings in VHR remote sensing images.

Detection Analysis and Efficiency of New Generation of Weather Radar over High Mountains

Based on years of use experience of previous 713 weather radar in Mount Tai,the optimal detection elevation and minimum detection height of the new generation of weather radar CINRAD/CD in Mount Tai were calculated and analyzed. According to the comparative analysis of maximum reflectance factor( DBZM) and vertical integrated liquid water based on cells( C-VIL) of the new generation of weather radar in Mount Tai( CD radar in Mount Tai) and Jinan( SA radar in Jinan),differences of echoes observed by the radar at the same elevation at different altitudes were revealed. It is pointed out that the ability of CD radar in Mount Tai to detect precipitation echoes was inadequate because the radar used the same elevation of above 0. 5° as the radar in the plain. Moreover,some suggestions were put forward to improve the efficiency of CD radar in Mount Tai.

A discussion on improving typhoon observation through radar by scanning the negative elevation angle

Certain feasibilities and features were discussed in typhoon detection by radar with a negative elevation angle according to the relationship between the remote detecting range and the elevation angle of the new generation weather radar, in order to rectify the disadvantages of detecting capability for remote low-level echo with a lowest elevation angle of 0.5° in the common detecting mode. The data obtained from detecting the typhoon of Haitang and Changmi with radar for their negative elevation angles and the observed data for the common lowest elevation angle of 0.5° were compared to each other. The results showed that the detection of remote low level cloud system with radar could be improved by using the negative elevation angle, and the structure and the evolution trend of a typhoon could be better judged. The increasing degree of detection for negative elevation angles in the current volume scanning mode should be helpful for predicting the intensity and developing trend of windstorms, to further improve the capability of warning and nowcasting. The detection of negative elevation angle could also help reveal the development and change of typhoon＇s low level cloud system. As far as the typhoons of Haitang and Changmi were concerned, the detecting area of Changmi was increased by 1.09 times with the negative elevation angle of 0.31°, compared with the elevation angle of 0.48° if the threshold value for the sea echo within 100 km was eliminated. Several volume scans of Haitang were increased by 2.1%-7.9% for the negative elevation angle of 0.36° compared with the elevation angle of 0.49° . Therefore, the radar detecting capability of typhoons could be improved by the detection of negative elevation angles to some extent. This could make up for the disadvantages of a low detecting capability for remote low-level echo in the common detecting mode. At the same time, a negative elevation angle could be easily influenced by the ground clutter and the close sea wave clutter which interfered with the assessment of the typhoon structure at times. Assessing these advantages and disadvantages, some advantages for using negative elevation angle were discovered from the observation of the typhoons Haitang and Changmi, if the negative elevation angle with radar was selected reasonably in some conditions. As a result, a certain value arose for improving and monitoring the early warning system for typhoons, paying close attention to the detection of negative elevation angles.

Discussion about the Application of CINRAD / SA Radar Using Negative Elevation Angle Mode in Observation of Tropical Cyclone

The study aimed to discuss the application of CINRAD/SA radar using negative elevation angle mode in observation of tropical cyclone. Firstly, the calculation formula of the lowest detecting height of CINRAD/SA radar was educed, and then the application of negative angle mode in Changle Radar Station was introduced. Through analyzing different observing abilities for tropical cyclone detected at different elevation angles, we discussed the limitation of CINRAD/SA radar using negative angle mode, and finally proposed some suggestions on CINRAD/SA radar using nega- tive elevation angle mode to observe tropical cyclone.

Fast recognition algorithm of underwater micro-terrain based on ultrasonic detection

An algorithm was proposed to fast recognize three types of underwater micro-terrain, i.e. the level, the gradient and the uneven. With pendulum single beam bathymeter, the hard level concrete floor, the random uneven floor and the gradient wood panel (8-) were ultrasonically detected 20 times, respectively. The results show that the algorithm is right from fact that the first clustering values of the uneven are all less than the threshold value of 60.0% that is obtained by the level and gradient samples. The algorithm based on the dynamic clustering theory can effectively eliminate the influences of the exceptional elevation values produced by the disturbances resulted from the grazing angle, the characteristic of bottom material and environmental noises, and its real-time capability is good. Thus, the algorithm provides a foundation for the next restructuring of the micro-terrain.

基于手机传感器的多参数电梯检测方法

电梯是高层建筑必不可少的垂直交通工具,而定期对电梯进行检测与检验尤其重要。传统的电梯检测方法需要电梯检测人员携带大量硬件设备,过程烦琐且不便。为解决此问题,本文提出了一种基于智能手机的电梯检测方法,利用手机内置传感器可获取电梯检测的关键参数。对样本数据进行对比分析,其结果显示样本数据接近专用设备所测的数据。因此,使用智能手机传感器可以方便有效地检测电梯的运行特性,从而跟进科技发展,避免机械浪费。

基于OR-CNN的电动车进入电梯危险行为检测系统设计

在楼宇中电动车不论是在电梯中还是在楼层中自燃爆炸都会给人们造成严重的危害,是一个重大的危险源。目前虽然在电梯口贴上了“电动车禁止进入”的提示标志,但效果不佳。因此,设计了基于OR-CNN的电动车进入电梯危险行为检测系统,以满足管理部门对禁止电动车进入电梯的需求。基于OR-CNN网络的电动车检测模型将RoI池化层替换为PORoI,PORoI池化单元通过先验知识将目标划分为5个部分,融合各个部分的特征信息,更好地完成在遮挡环境下的目标检测任务。此外,系统在发现违规行为时会使电梯门处于禁关状态,并发出警报提醒,对违规行为的视频段进行抽帧处理并记录存档,以便事后追责,实现智能化管理。测试结果表明,与YOLOv5相比,所设计的检测系统在遮挡情况下的电动车检测准确率明显提高,更适应电梯等狭小环境中目标遮挡的情况。

基于轨道结构高差特征值的CRTSⅡ型板离缝定量检测方法

高速铁路无砟轨道的离缝病害对行车安全至关重要,对此提出一种基于轨道板-支承层相对高差特征值的离缝定量检测方法。通过集成线结构光等传感器构建离缝检测原型系统设备,用于采集轨道轮廓线;将轨道轮廓线按轨道结构分成三段并基于RANSAC算法分别进行拟合,拟合直线相交两点间的距离以此作为轨道结构高差特征值;以轨枕位置为里程定位基准,对比同一区段内轨道结构高差特征值的多次检测结果,通过变化量精确计算离缝病害的长度和高度值。在此基础上进行线路试验,结果表明:检测系统多次重复测量结果的标准偏差系数稳定在2%以内;在95%的置信度下,重复测量偏差小于0.25 mm;将检测结果与铁路部门当期的病害库进行对比,离缝高度和长度检测结果的平均绝对误差分别为0.24 mm和0.34 m,均方对数误差值分别为0.011和0.017。

基于双目视觉的吊卡识别及其方位检测方法

针对钻井自动化中的吊卡方位需要人工参与,存在识别定位缓慢和准确性差等问题,建立了基于双目视觉的吊卡自动识别和方位检测系统。通过搭建双目相机视觉检测系统,对获取的吊卡图像进行标定、立体校正和立体匹配,进而获取吊卡图像的深度图。在原YOLOv5s目标检测算法的主干网络中引入卷积注意力机制模块,将要识别的目标即吊卡图像进行增强,进一步提高吊卡的识别准确率,结合深度图计算出吊卡中心位置相对机械手的距离和偏转角度,从而实现自动送管。通过在钻井平台的试验,验证了吊卡识别和检测方法的有效性。所得结论可为钻井平台自动化程度的进一步提高提供技术借鉴。

曳引式电梯轮槽磨损故障特点、成因及检测技术

曳引式电梯在驱动形式上为曳引驱动,使电梯完成升降功能。曳引式电梯在运行过程中会受相关因素影响而出现运行故障,常见的故障为电梯轮槽磨损,若不及时进行维护,则容易影响电梯的运行效率与安全。本文基于曳引式电梯为例,深入探究电梯轮槽磨损的特点、成因与检测技术。

基于加速度传感器的轿厢意外移动保护装置的检测方法

简要介绍一种轿厢意外移动保护装置的检测方法。根据国标对意外移动距离的相关要求,融合加速度传感器和陀螺仪的数据,并对轿厢意外移动时的加速度等数据进行双重积分获得位移,进而得出轿厢意外移动的距离,该方法适用于有机房和无机房电梯。最终通过对比国标要求,判定轿厢意外移动保护装置是否符合要求。

一则电梯突发困人事件的案例分析

针对某小区电梯突发困人事件的案例,介绍了电梯困人概况,分析了电梯困人产生的原因,提供了检验方案以及整改措施。最后根据此类问题产生的原因,对该案例所涉及电梯的使用进行了建议。

电梯安全性能定量测试分析仪的设计及应用

本文梳理了TSG T7001—2023《电梯监督检验和定期检验规则》和GB/T 7588.1—2020《电梯制造与安装安全规范第1部分:乘客电梯和载货电梯》中对几个重要检验项目的技术要求,这些检验项目需要对电梯的加速度值进行定量测试分析。传统的测试方法存在一定局限性,检验效率和检验质量不高。本文研发了一种电梯安全性能定量测试分析仪,能够对电梯三轴加速度进行测量,对数据进行分析,得到轿厢运行的振动峰峰值、运行速度、行程、制停距离、减速度和制停时间等数值。可以实现对电梯轿厢意外移动保护装置安全性能的检测,具备自动触发和人工触发2种模式,可实现累积误差消除,自动判断轿厢启停,对制动触发点自动进行捕捉,有效地提升检验效率和检验精度。

电梯永磁曳引机的在线退磁检测

永磁同步电机具有功率大的优势,但在电梯曳引应用中存在机械强度低与不可逆退磁的问题。其中,退磁问题会直接影响电梯运行安全,甚至导致电梯失速与冲顶。鉴于此,提出了一种基于变参数SMO的在线退磁检测方法,根据永磁同步电机在电梯曳引中的结构特征,选择采用改进饱和函数与可变开关增益的方式解决SMO应用中的抖振问题。结果表明,新型在线退磁检测方法具有较高的检测精度,可满足电梯永磁曳引机在线检测工作要求。

Global detection of large lunar craters based on the CE-1 digital elevation model

Craters, one of the most significant features of the lunar surface, have been widely researched because they offer us the relative age of the surface unit as well as crucial geological information. Research on crater detec- tion algorithms （CDAs） of the Moon and other planetary bodies has concentrated on detecting them from imagery data, but the computational cost of detecting large craters using images makes these CDAs impractical. This paper presents a new approach to crater detection that utilizes a digital elevation model instead of images; this enables fully automatic global detection of large craters. Craters were delineated by terrain attributes, and then thresholding maps of terrain attributes were used to transform topographic data into a binary image, finally craters were detected by using the Hough Transform from the binary image. By using the proposed algorithm, we produced a catalog of all craters ≥ 10 km in diameter on the lunar surface and analyzed their distribution and population characteristics.

基于数字高程模型的淤地坝地区河网提取方法

淤地坝作为黄土高原水土流失严重地区重要而独特的治沟工程,具有滞洪、拦淤、蓄水等作用,但在该地区利用数字高程模型(DEM)进行流域水系分析时,提取的河网会受淤地坝的干扰出现河网偏移、错位问题。本文提出一种通过对淤地坝进行检测并修正DEM的自动化方法来实现河网的有效提取。该方法先利用区域生长和形态学算法提取河道中心线,以缩小淤地坝检测范围,再基于改进的直线段检测(LSD)算法以及角度滤波确定淤地坝候选轮廓直线,然后构建十字模型定位淤地坝,最后对淤地坝所在栅格高程进行修正以提取河网。该方法在黄土高原两个典型样区中检测淤地坝的F1值分别为86.67%和86.95%,相比于仅使用填充、裂开的两种河网提取方法,本文方法能有效解决河网偏移问题。结果表明,本文方法提取的河网结果更符合真实地形,可为黄土高原淤地坝地区的流域数字地形分析技术提供有益补充与支持。

采用多级负载评估电梯永磁同步电机的退磁

为了研究电梯永磁同步电机的退磁状态评估,提出一种基于参数自调节滑模算法的永磁同步电机退磁故障检测方法,通过获取电机多级负载状态下的电压与电流数据,对电机的空载反电动势进行估算,并作为退磁评估依据。方法在使用传统滑模观测器(SMO)估算电机扩展反电动势的基础上,将不同负载下的永磁体的工作点变动及定子电阻、电感参数的变化纳入考虑,构建了电机空载反电动势估算误差方程。通过对滑模算法中的电阻、电感参数进行调节,来抵消上述因素的影响,进而实现对电机空载反电动势的获取,并根据电阻、电感参数对估算结果的不同影响特性,实现了电阻与电感参数的自调节。经过仿真分析与实验验证,该方法能够在无须测量编码器数据的前提下,对电机内部永磁体的性能进行量化分析,方法适用于局部退磁与均匀退磁两种故障类型,且准确度高,可用于电梯永磁同步电机外置退磁检测。

电梯曳引钢带钢丝不同截面积损伤的漏磁检测

电梯曳引钢带长周期运行后,钢带内部钢丝会出现断丝、断股、磨损等类型损伤,这些损伤会严重影响电梯的运行安全,为此,开展了电梯曳引钢带钢丝不同截面积损伤的漏磁检测。通过建立含有不同截面积损伤的电梯曳引钢带有限元模型,得到了不同截面积缺陷的漏磁场空间分布状态和变化规律,然后在实验室条件下对钢带钢丝不同缺陷的漏磁信号进行检测。理论分析和试验结果表明,漏磁检测能有效检测出电梯曳引钢带钢丝的不同截面积损伤,为电梯曳引钢带运行维护提供一定的决策依据。