Automatic Miscalibration Detection and Correction of LiDAR and Camera Using Motion Cues

This paper aims to develop an automatic miscalibration detection and correction framework to maintain accurate calibration of LiDAR and camera for autonomous vehicle after the sensor drift.First,a monitoring algorithm that can continuously detect the miscalibration in each frame is designed,leveraging the rotational motion each individual sensor observes.Then,as sensor drift occurs,the projection constraints between visual feature points and LiDAR 3-D points are used to compute the scaled camera motion,which is further utilized to align the drifted LiDAR scan with the camera image.Finally,the proposed method is sufficiently compared with two representative approaches in the online experiments with varying levels of random drift,then the method is further extended to the offline calibration experiment and is demonstrated by a comparison with two existing benchmark methods.

Single Photon Detection Technology in Underwater Wireless Optical Communication:Modulation Modes and Error Correction Coding Analysis

This study explores the application of single photon detection(SPD)technology in underwater wireless optical communication(UWOC)and analyzes the influence of different modulation modes and error correction coding types on communication performance.The study investigates the impact of on-off keying(OOK)and 2-pulse-position modulation(2-PPM)on the bit error rate(BER)in single-channel intensity and polarization multiplexing.Furthermore,it compares the error correction performance of low-density parity check(LDPC)and Reed-Solomon(RS)codes across different error correction coding types.The effects of unscattered photon ratio and depolarization ratio on BER are also verified.Finally,a UWOC system based on SPD is constructed,achieving 14.58 Mbps with polarization OOK multiplexing modulation and 4.37 Mbps with polarization 2-PPM multiplexing modulation using LDPC code error correction.

Scale effect removal and range migration correction for hypersonic target coherent detection

The detection of hypersonic targets usually confronts range migration(RM)issue before coherent integration(CI).The traditional methods aiming at correcting RM to obtain CI mainly considers the narrow-band radar condition.However,with the increasing requirement of far-range detection,the time bandwidth product,which is corresponding to radar’s mean power,should be promoted in actual application.Thus,the echo signal generates the scale effect(SE)at large time bandwidth product situation,influencing the intra and inter pulse integration performance.To eliminate SE and correct RM,this paper proposes an effective algorithm,i.e.,scaled location rotation transform(ScLRT).The ScLRT can remove SE to obtain the matching pulse compression(PC)as well as correct RM to complete CI via the location rotation transform,being implemented by seeking the actual rotation angle.Compared to the traditional coherent detection algorithms,Sc LRT can address the SE problem to achieve better detection/estimation capabilities.At last,this paper gives several simulations to assess the viability of ScLRT.

Building Change Detection Improvement Using Topographic Correction Models

In the change detection application of remote sensing, commonly the variation in the brightness values of the pixels/objects in bi-temporal image is used as an indicator for detecting changes. However, there exist effects, other than a change in the objects that can cause variations in the brightness values. One of the effects is the illumination difference on steep surfaces mainly steeproofs of houses in very high resolution images, specifically in off-nadir images. This can introduce the problem of false change detection results. This problem becomes more serious in images with different view-angles. In this study, we propose a methodology to improve the building change detection accuracy using imagery taken under different illumination conditions and different view-angles. This is done by using the Patch-Wise Co-Registration (PWCR) method to overcome the misregistration problem caused by view-angle difference and applying Topographic Correction (TC) methods on pixel intensities to attenuate the effect of illumination angle variation on the building roofs. To select a proper TC method, four of the most widely used correction methods, namely C-correction, Minnaert, Enhanced Minnaert (for slope), and Cosine Correction are evaluated in this study. The results proved that the proposed methodology is capable to improve the change detection accuracy. Specifically, the correction using the C-correction and Enhanced Minnaert improved the change detection accuracy by around 35% in an area with a large number of steep-roof houses imaged under various solar angles.

Enhanced Face Detection Technique Based on Color Correction Approach and SMQT Features

Face detection is considered as a challenging problem in the field of image analysis and computer vision. There are many researches in this area, but because of its importance, it needs to be further developed. Successive Mean Quantization Transform (SMQT) for illumination and sensor insensitive operation and Sparse Network of Winnow (SNoW) to speed up the original classifier based face detection technique presented such a good result. In this paper we use the Mean of Medians of CbCr (MMCbCr) color correction approach to enhance the combined SMQT features and SNoW classifier face detection technique. The proposed technique is applied on color images gathered from various sources such as Internet, and Georgia Database. Experimental results show that the face detection performance of the proposed method is more effective and accurate compared to SFSC method.

An Efficient Overflow Detection and Correction Scheme in RNS Addition through Magnitude Evaluation

Number Systems are media for representing numbers;the popular ones being the Weighted Number Systems (WNS), which sometimes propagate carries during arithmetic computations. The other category, Un-Weighted Number Systems, of which the Residue Number System (RNS) belongs, do not carry weights but have not yet found widespread usage in general purpose computing as a result of some challenges;one of the main challenges of RNS is overflow detection and correction. The presence of errors in calculated values due to such factors as overflow means that systems built on this number system will continue to fail until serious steps are taken to resolve the issue. In this paper, a scheme for detecting and correcting overflow during RNS addition is presented. The proposed scheme used mixed radix digits to evaluate the magnitude of the addends in order to detect the occurrence of overflow in their sum. The scheme also demonstrated a simplified technique of correcting the overflow in the event that it occurs. An analysis of the hardware requirements and speed limitations of the scheme showed that it performs considerably better in relation to similar state of art schemes.

An automatic seismic signal detection method based on fourth-order statistics and applications

Real-time, automatic, and accurate determination of seismic signals is critical for rapid earthquake reporting and early warning. In this study, we present a correction trigger function（CTF） for automatically detecting regional seismic events and a fourth-order statistics algorithm with the Akaike information criterion（AIC） for determining the direct wave phase, based on the differences, or changes, in energy, frequency, and amplitude of the direct P- or S-waves signal and noise. Simulations suggest for that the proposed fourth-order statistics result in high resolution even for weak signal and noise variations at different amplitude, frequency, and polarization characteristics. To improve the precision of establishing the S-waves onset, first a specific segment of P-wave seismograms is selected and the polarization characteristics of the data are obtained. Second, the S-wave seismograms that contained the specific segment of P-wave seismograms are analyzed by S-wave polarization filtering. Finally, the S-wave phase onset times are estimated. The proposed algorithm was used to analyze regional earthquake data from the Shandong Seismic Network. The results suggest that compared with conventional methods, the proposed algorithm greatly decreased false and missed earthquake triggers, and improved the detection precision of direct P- and S-wave phases.

Topographically derived subpixel-based change detection for monitoring changes over rugged terrain Himalayas using AWiFS data

Continuous and accurate monitoring of earth surface changes over rugged terrain Himalayas is important to manage natural resources and mitigate natural hazards.Conventional techniques generally focus on per-pixel based processing and overlook the sub-pixel variations occurring especially in case of low or moderate resolution remotely sensed data.However,the existing subpixel-based change detection(SCD)models are less effective to detect the mixed pixel information at its complexity level especially over rugged terrain regions.To overcome such issues,a topographically controlled SCD model has been proposed which is an improved version of widely used per-pixel based change vector analysis(CVA)and hence,named as a subpixel-based change vector analysis(SCVA).This study has been conducted over a part of the Western Himalayas using the advanced wide-field sensor(AWiFS)and Landsat-8 datasets.To check the effectiveness of the proposed SCVA,the cross-validation of the results has been done with the existing neural network-based SCD(NN-SCD)and per-pixel based models such as fuzzybasedCVA(FCVA)andpost-classification comparison(PCC).The results have shown that SCVA offered robust performance(85.6%-86.4%)as comparedtoNN-SCD(81.6%-82.4%),PCC(79.2%-80.4%),and FCVA(81.2%-83.6%).We concluded that SCVA helps in reducing the detection of spurious pixels and improve the efficacy of generating change maps.This study is beneficial for the accurate monitoring of glacier retreat and snow cover variability over rugged terrain regions using moderate resolution remotely sensed datasets.

Parallel Algorithms for Residue Scaling and Error Correction in Residue Arithmetic

In this paper, we present two new algorithms in residue number systems for scaling and error correction. The first algorithm is the Cyclic Property of Residue-Digit Difference (CPRDD). It is used to speed up the residue multiple error correction due to its parallel processes. The second is called the Target Race Distance (TRD). It is used to speed up residue scaling. Both of these two algorithms are used without the need for Mixed Radix Conversion (MRC) or Chinese Residue Theorem (CRT) techniques, which are time consuming and require hardware complexity. Furthermore, the residue scaling can be performed in parallel for any combination of moduli set members without using lookup tables.

面向溢流与井漏监测的钻井液池体积趋势校正方法

钻井液池体积是溢流和井漏监测的常用参数。在钻井正常施工时,随着井筒容积的变化以及井筒内钻井液与地层流体的交换,钻井液池体积会产生与溢流和井漏发生时相似的“类风险趋势”,导致现有的溢流与井漏监测方法易产生误报,降低了溢流与井漏监测方法在现场应用时的有效性。针对此问题,文章首先分析了导致池体积产生“类风险趋势”的原因,建立了钻进与起下钻工况下池体积的正常变化模型,利用建立的模型校正池体积的“类风险趋势”,降低溢流与井漏监测的误报率;然后,建立了基于规则推理的溢流与井漏监测模型,用于测试池体积的“类风险趋势”对溢流与井漏监测的影响。利用现场实测的4500组钻井数据,采用基于规则推理的溢流与井漏监测方法开展了风险监测实验,结果表明,在进行池体积趋势校正后,溢流与井漏风险监测的误报率由10.03%降低至3.06%。

考虑碾压参数影响的压实质量连续检测结果修正研究

为降低压路机行驶速度、行驶方向、振动频率等碾压参数引起的压实质量连续检测结果CMV误差,以动态变形模量E_(vd)为修正目标,基于极限梯度提升XGBoost算法和多元线性回归方法分别建立CMV修正模型,对比修正模型的适用性,并分析各碾压参数对CMV误差的影响。研究表明,XGBoost修正结果与E_(vd)的相关系数高达0.8且误差仅为1.7%,而多元线性回归修正结果与E_(vd)的相关系数仅为0.65且误差高达9.2%,XGBoost修正模型更适用于降低由碾压参数引起的CMV检测误差。高速行驶会造成CMV存在显著的负误差,行驶方向的不同使CMV产生大小相当但正负相反的误差,振动频率对CMV误差的影响是非线性的且与频率的取值有关。最后根据现场压实检测数据的修正结果验证了XGBoost修正模型对降低CMV检测误差的可靠性。

应用于高性能延迟锁相环的占空比修正电路设计

设计了一款应用于高性能延迟锁相环的占空比修正电路。该电路主要由差分放大电路、占空比调整电路、缓冲器电路和占空比检测电路组成,采用TSMC 40 nm CMOS工艺和1.1 V的电源电压。仿真的结果表明,时钟频率2 GHz~8 GHz,占空比20%~80%的输入时钟信号,经过占空比修正电路调节后,输出时钟信号占空比变为50%±0.2%,可应用于高性能延迟锁相环中。

改进Mask RCNN的盾构隧道渗漏水检测方法

渗漏水是盾构隧道结构存在潜在损伤或缺陷的重要表征,快速、准确检测出渗漏水位置,对隧道安全运营和维护具有重要意义。现有的方法大多采用光学影像对隧道渗漏水进行检测,受隧道内空间和光线条件限制,难以获得高质量病害图片。因此,本文提出了一种基于激光点云数据与改进Mask RCNN相结合的渗漏水检测方法。首先对激光点云反射强度进行修正;然后生成灰度图像并建立渗漏水病害数据集;最后在Mask RCNN算法中引入空洞卷积和变形卷积,实现了隧道渗漏水病害的快速检测。利用某地铁采集的数据进行验证,结果表明,本文提出的改进Mask RCNN算法相较于原始算法和FCN算法检测精度均有明显提升,在盾构隧道渗漏水识别方面性能表现较好。

基于颜色校正和深度信息去雾的水下感知系统

针对水下距离感知任务真实训练数据缺乏,水下目标感知任务目标模糊、密集、多尺度的问题,提出一种基于颜色校正和深度信息去雾的水下视觉感知系统。设计了一种改进的融合增强方法,并建立了一个水下单目图像数据集,以解决距离感知任务数据不足的难点。设计了一种基于深度信息的去雾方法,结合水下成像模型对图像进行去雾处理,提升图像质量。设计了一种基于中心点检测的通道重排网络,将卷积神经网络中浅层的详细特征完全集成到深层中,且无需锚框,增强对小目标、密集目标的特征提取能力。实验表明,该系统可从水下图像中恢复真实陆地色彩,准确感知水下场景相对距离,并实现域内和跨域高精度目标感知,在URPC数据集上取得了78.2%的域内目标检测精度,比基准CenterNet高出4.6%,在UTTS数据集上取得81.5%跨域目标检测精度,证明了该系统的有效性。

基于直线检测的航向角误差校正方法

提出了一种基于直线检测模型的多传感器数据融合航向角随机误差校正方法,旨在提高农林环境下低成本传感器组成的定位平台的精度。该方法通过调整直线检测阈值来实现状态的动态调整,以提高导航系统的鲁棒性和精确性。再将多传感器数据通过卡尔曼滤波融合,实现航向角随机误差的校正。试验结果表明,该方法在不同路径和速度下能有效降低航向角误差提高定位精度。在直线行进试验中,本方法的定位精度保持在5cm以内,航向角误差在5°以内。在矩形行进试验中,本方法的轨迹与差分RTK方法相近,平均误差仅为2.7cm,标准差为3.9cm。这一航向角校正方法为农业机械和车辆环境中的自主操作提供了有力支持。它能够适应不同的环境条件,提高导航系统的性能和测量准确性。

一种面向鱼眼图像的行人检测算法

鱼眼镜头非线性光学畸变导致鱼眼图像行人检测算法精度低,且校正算法也无法完全克服鱼眼图像的边缘严重变形。针对上述问题,文中以Faster R-CNN架构为基础,建立了鱼眼图像校正光路模型。针对鱼眼图像畸变,提出一种基于微分方程的鱼眼图像校正模型,并提出一种改进算法用于鱼眼图像的行人检测。构建了ResNet 50融合特征金字塔网络结构,以增强网络的多尺度特征提取能力,提高网络对行人小目标的定位和识别能力;优化平滑L1损失函数解决大梯度难学样本与小梯度易学样本间的不平衡问题,提高训练效果。实验结果表明,文中算法与现有鱼眼图像行人检测算法相比,检测精度提高了39.68%。在边缘轻微畸变及小尺度行人的检测精度可以达到90%以上,有助于提高极端条件下鱼眼图像的行人检测性能。

一种改进的行人航迹推算算法研究

针对行人在室内定位不准确的问题,提出一种改进的行人航迹推算算法。在步数检测阶段,提出一种基于运动分割的三阈值峰值检测法,实现了行人在不同运动状态下步数的精准检测。通过使用改进的Weinberg模型实现步长估算。并提出一种基于主方向假设的航向角修正算法,实现行人的航向角修正。最后综合步数、步长和航向角信息实现室内行人的航迹推算。实验结果表明,改进的行人航迹推算算法在室内有较好的稳定性,在室内的平均定位误差<5%,较传统PDR算法在平均定位误差上降低了9.53%,提高了行人在室内定位的精度。

基于三维激光扫描技术的竣工盾构隧道渗漏水检测

渗漏水是隧道常见的病害,长期渗漏会导致隧道结构开裂、裂缝、钢筋腐蚀,威胁到隧道运营安全,因此对其检测一直受到重视。本文利用三维激光扫描技术实现了竣工盾构隧道渗漏水的位置及面积的自动检测。首先采用架站式三维激光扫描仪采集竣工隧道点云,基于修正后的反射强度值生成隧道衬砌表面灰度图,再采用膨胀法与腐蚀法对灰度图像进行预处理;然后利用连通域算法计算渗漏水位置及其面积;最后结合实际工程验证本文方法的实用性及准确性。结果表明,应用本文方法竣工隧道的渗漏水检测准确率达92%。

基于计算机视觉的混凝土表观裂缝识别和宽度测量

对钢筋混凝土(reinforcement concrete, RC)结构表观裂缝的高效识别可以为结构震损快速评估提供佐证。无论在地震现场还是实验室环境,此类工作均表现出量大、重复的特征,适合利用计算机视觉技术完成,以弥补人工方式低效、不确定性强的劣势。以消费级相机输出图像作为数据源,融合U-Net和VGG-16构造适用于混凝土表观裂缝识别的卷积神经网络(convolutional neuralnetwork, CNN)模型,依托多类型RC构件裂缝图像数据库完成模型训练和测试。利用形态学运算、Otsu阈值分割等技术进一步优化裂缝识别结果作为宽度测量的输入数据。为降低相机光轴与裂缝平面不垂直带来的裂缝宽度测量误差,通过特定靶标对原始图像进行透视误差校正,经检验,透视误差校正后的裂缝宽度测量的平均偏差最大可降低约25%。

基于YOLOv8的女西裤板型弊病检测与修正

在服装定制成衣试穿环节,由于人体体型多样性,经常出现服装试穿不合身的情况。为协助改衣师快速准确找到板型问题及修改方案,以女西裤为例,收集常见的女西裤弊病图像(前裆堆量明显、前裆猫须明显、后片夹裆和后片大腿根堆斜褶明显)作为数据集,并采用深度学习算法中的YOLOv8模型进行实验。研究表明:模型测试阶段,精确度、召回率、平均精度均值(I_(ou)=50%)均达到98%以上,同时结合弊病修正实验验证了弊病修正建议的合理性,实现了女西裤板型弊病的智能检测与修正。