Performance and scalability of Fourier domain optical coherence tomography acceleration using graphics processing units

Fourier domain optical coherence tomography (FD-OCT) provides faster line rates, better resolution, and higher sensitivity for noninvasive, in vivo biomedical imaging compared to traditional time domain OCT (TD-OCT). However, because the signal processing for FD-OCT is computationally intensive, real-time FD-OCT applications demand powerful computing platforms to deliver acceptable performance. Graphics processing units (GPUs) have been used as coprocessors to accelerate FD-OCT by leveraging their relatively simple programming model to exploit thread-level parallelism. Unfortunately, GPUs do not "share" memory with their host processors, requiring additional data transfers between the GPU and CPU. In this paper, we implement a complete FD-OCT accelerator on a consumer grade GPU/CPU platform. Our data acquisition system uses spectrometer-based detection and a dual-arm interferometer topology with numerical dispersion compensation for retinal imaging. We demonstrate that the maximum line rate is dictated by the memory transfer time and not the processing time due to the GPU platform's memory model. Finally, we discuss how the performance trends of GPU-based accelerators compare to the expected future requirements of FD-OCT data rates.     

CCD高速信号采集和处理系统

介绍了一种用于线阵ＣＣＤ高速数据采集的方法和系统，用双ＣＣＤ图象存储体逐场存储ＣＣＤ信号，ＣＰＵ直接对存储体进行寻址运算的原理，并用８０９７单片机构成的１６位数据总线ＣＰＵ单元，实现了ＣＣＤ信号的高速采集及处理。     

带钢表面缺陷视觉检测系统关键技术研究

为满足钢板表面缺陷在线检测系统宽幅面、高速、高分辨率的检测要求，讨论了基于线阵CCD的钢板表面缺陷视觉检测系统实现的关键技术；优化设计了视觉检测系统的光学照明部分，以检测不同类型的缺陷。通过软件系统的特殊设计，以保证实时在线检测。针对缺陷图像低对比度、高噪声的特点，提出了基于灰度统计特性的图像边缘检测方法，并实现了对缺陷图像的自适应阈值分割。依据图像的缺陷统计特性，定义了缺陷的灰度、几何等特征量，用于缺陷分类。本系统样机已在实验室环境下运行。     

基于图像处理技术的五金件表面缺陷检测研究

为实现图像处理技术在表面缺陷检测中的应用,取代传统人工检测的方式,设计开发了一套五金件表面缺陷检测系统,研究了镀镍五金件表面图像在线处理算法,包括边缘检测算法、位置修正算法、标定算法和检测算法,实现了五金件表面缺陷视觉检测.同时提出一种阈值反馈算法,用实际缺陷值反馈验证理论缺陷值,最终得到可靠的缺陷阈值,判断表面缺陷....     

包装箱型号标记缺陷检测系统设计与实现

目的 为了实现空调包装箱上型号标记缺陷的实时动态检测,基于图像处理技术设计包装箱型号标记缺陷检测系统.方法 基于AM5728控制器设计控制系统硬件平台,主要包括控制单元、图像采集与处理单元、成像单元等,并进行实际测试研究.采用几个关键方法,包括图像增强处理、形态学、缺陷检测、动态阈值分割算法等,并根据包装箱型号标记图像特征选择配准区域,同时给出一种动态阈值分割算法,利用各种算法实现缺陷检测.结果 采集了250个包装箱条码样本,采用文中方法获取到了监测数据,正确率高达97.2％,漏检率为0.结论 该方法具有较高的可靠性、通用性,可实现包装箱型号标记的缺陷快速检测,解决了空调包装箱上的型号标记实时动态缺陷检测的实际工程问题.     

Study of the Choice of Excitation Frequency for Sub Surface Defect Detection in Electrically Thick Conducting Specimen Using Eddy Current Testing

Understanding the scope and limitations of non-destructive testing procedure is essential for selecting the appropriate test parameters for material inspection. This paper presents the scope of material (\( \delta_{s} \)) and probe dependent (\( \delta_{t} \)) penetration depths for determining the optimal test frequency (\( f_{opt} ) \) for detection of sub surface defects in electrically thick conducting specimens. Numerical modelling is carried out for a pancake coil above an electrically thick aluminium plate, \( t/\delta_{t} \)?>?1, to study the influence of the EC probe and defect location (\( t_{df} \)) on the test frequency for near and deep sub surface defects. The study concludes that the optimal test frequency, \( f_{opt} \) for detection of deep sub surface defects (\( t_{df} /t \approx 1 \)) is determined by the probe dependent skin depth, \( \delta_{t} \), and the plate thickness is related to \( f_{opt} \) by, \( t \propto 1/\sqrt {f_{opt} } \). The numerical observations were experimentally validated for machined sub surface notches on a 10 mm thick (\( t \)) aluminium plate.     

基于YOLOv4算法的药用空心胶囊表面缺陷检测方法

目的 为提升质检过程中药用空心胶囊的表面缺陷检测精度及其自动化水平。方法 通过设计高质量图像采集方案来避免胶囊表面出现光斑,以此构建药用空心胶囊缺陷数据集。基于YOLOv4算法,建立深度学习检测模型,利用多尺度特征提取以及训练策略,增强对小目标缺陷检测的鲁棒性。采用K-means++聚类算法更新锚框初始值,以提高模型对胶囊表面缺陷的预测性能。结果 实验结果表明,提出的胶囊缺陷检测方法能够准确判别胶囊好坏,并能检测出其表面的凹陷、孔洞、划痕、污点和接口缺损等5类缺陷,其中对于胶囊有无缺陷的平均精确均值达99.05%,各缺陷类型的平均精确率为91.81%,而每秒检测图像可达22张。与其他典型的目标检测方法相比,文中方法在检测速度和精度上都有一定优势。结论 文中所提出的基于YOLOv4的缺陷检测方法实现了对药用空心胶囊多类型缺陷的分类与定位,具有较好的检测效果和稳定性,在满足生产质量管控要求的同时,可大幅降低人工成本。     

精确测量金属镀层工件表面缺陷的图像处理方法

设计了一套自动测量表面缺陷的系统．以面阵CCD采集图像,实现了对金属镀层工件表面图像的双面、高速、高分辨率测量．系统的图像处理采用模块化程序设计,方便实现工件表面精确测量的特定要求．通过两次分别选取闽值,采用经过改进的大津阈值分割法求取特征分割阈值时,能够十分理想地提取工件上的微小缺陷,实现了对金属工件表面的精确测量．该系统针对不同形状、不同镀层的工件表面可以调整LED照明光源的光强,在图像处理中可以调整去光圈和特征检测的灵敏度．通过方便调整系统的技术参数,满足工业现场的具体要求．以钕铁硼小型工件缺陷检测作为应用实例验证了该系统的可行性．     

基于改进YOLOv4算法的轮毂表面缺陷检测

汽车轮毂加工过程中产生的表面缺陷严重影响整车的美观性及服役性能,针对人工检测效率低、漏检率高的问题,提出一种基于改进YOLOv4算法的轮毂表面缺陷检测方法。构建了轮毂缺陷数据集,其包含6种表面缺陷,由2346张4928×3264pixel的图像组成;采用K-means方法进行先验框聚类,并针对YOLOv4算法在纤维、粘铝等小尺度缺陷上检测精度不足问题,在原网络Neck部分引入细化U型网络模块(TUM)和注意力机制,用于增强有效特征并抑制无效特征,强化多尺度特征提取与融合,改善特征处理过程中可能存在的小目标信息丢失问题;基于该数据集,训练并测试不同算法的缺陷检测性能并验证改进模块的有效性。结果表明,该方法大幅提升了粘铝等小尺寸缺陷的检测能力,缺陷检测平均精度达到85.8%,与多种算法相比较检测精度最高。     

Pipeline Defect Detection Cloud System Using Role Encryption and Hybrid Information

Pipeline defect detection systems collect the videos from cameras of pipeline robots, however the systems always analyzed these videos by offline systems or humans to detect the defects of potential security threats. The existing systems tend to reach the limit in terms of data access anywhere, access security and video processing on cloud. There is in need of studying on a pipeline defect detection cloud system for automatic pipeline inspection. In this paper, we deploy the framework of a cloud based pipeline defect detection system, including the user management module, pipeline robot control module, system service module, and defect detection module. In the system, we use a role encryption scheme for video collection, data uploading, and access security, and propose a hybrid information method for defect detection. The experimental results show that our approach is a scalable and efficient defection detection cloud system.     

Rapid computation of sodium bioscales using gpu‐accelerated image reconstruction

Quantitative sodium magnetic resonance imaging permits noninvasive measurement of the tissue sodium concentration (TSC) bioscale in the brain. Computing the TSC bioscale requires reconstructing and combining multiple datasets acquired with a non‐Cartesian acquisition that highly oversamples the center of k‐space. Even with an optimized implementation of the algorithm to compute TSC, the overall processing time exceeds the time required to collect data from the human subject. Such a mismatch presents a challenge for sustained sodium imaging to avoid a growing data backlog and provide timely results. The most computationally intensive portions of the TSC calculation have been identified and accelerated using a consumer graphics processing unit (GPU) in addition to a conventional central processing unit (CPU). A recently developed data organization technique called Compact Binning was used along with several existing algorithmic techniques to maximize the scalability and performance of these computationally intensive operations. The resulting GPU+CPU TSC bioscale calculation is more than 15 times faster than a CPU‐only implementation when processing 256 × 256 × 256 data and 2.4 times faster when processing 128 × 128 × 128 data. This eliminates the possibility of a data backlog for quantitative sodium imaging. The accelerated quantification technique is suitable for general three‐dimensional non‐Cartesian acquisitions and may enable more sophisticated imaging techniques that acquire even more data to be used for quantitative sodium imaging. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 29–35, 2013.     

Illumination compensation methods for automatic fiber connector inspection

Unevenly distributed image illumination is a problem in an automatic machine visual inspection system used for fiber connector defect detection. This unevenly distributed illumination makes it difficult to find an appropriate horizontal threshold to convert a gray-scale image to a binary image to separate surface defects, such as blobs and scratches, from the background of the connector. This paper proposes some effective methods to compensate the unevenly distributed illumination problem so that standard image processing methods can be used effectively to detect the defects of the fiber connectors.     

基于改进YOLOv5的新材料地板表面缺陷检测研究

目的 提升质检过程中新材料地板的表面缺陷检测精度。方法 通过翻转、水平迁移和垂直迁移对采集到的缺陷图像进行扩充,构建新材料地板缺陷数据集。基于YOLOv5算法,增加一个预测头,使算法对微小缺陷更加敏感;其次在网络的特征融合层应用Swin Transformer模块,形成注意力机制预测头,提高网络特征提取效率;然后在网络主干末端加入SE模块,使网络提取有用的信息特征,提高模型精度。结果 实验结果表明,提出的新材料地板表面缺陷检测方法能够准确判别地板好坏,并能够识别出白色杂质、黑斑、边损、气泡胶等4类表面缺陷,各缺陷类型的平均精确均值为82.30%,比YOLOv5 Baseline提高了6.58%,相比其他典型目标检测算法也能够更准确和快速地识别地板表面缺陷。结论 通过改进的YOLOv5算法可以更准确地对地板表面缺陷进行分类与定位,从而大大提高工业质检效率。     

高速在线检测宽幅面彩色感光胶片质量

为降低线阵CCD拼接测量难度,提出改进的多光学系统线阵CCD拼接测量法。根据检测幅宽和精度要求,将较宽的被测幅面分成若干区域。各区域放置独立可调的光学成像系统及线阵CCD。调整各光学系统,使总成像范围覆盖整个被测幅面。几个线阵CCD同步驱动并行采集。图像信号通过PCI高速数据采集系统送入计算机。为提高判别速度,采用多尺度灰度形态学方法实现快速图像增强和缺陷判别,每秒钟可处理20幅2048像素200像素的8bit灰度图像。实验表明,可实现8个线阵CCD拼接检测幅面宽1.2m彩色感光胶片的表面质量,检测速度最高可达90m/min,可识别直径在0.1mm以上的缺陷,准确率在96%以上。     

Transient Eddy Current NDE System Based on Fluxgate Sensor for the Detection of Defects in Multilayered Conducting Material

This paper describes a novel transient eddy current non destructive evaluation (NDE) system for the detection of defects in a multilayered conducting material by using fluxgate magnetometer as a sensor. In conventional eddy current NDE, the depth of defect detection is restricted due to the excitation frequency and its associated skin depth. Similarly, in conventional pulsed eddy current testing the time derivative of the secondary magnetic field, which decays much faster than the magnetic field itself, is measured by the induction coil. However, in this work we use fluxgate magnetometer which measures magnetic field directly and double “D” differential excitation coil in order to enhance the depth of investigation. In addition to this, the other instruments such as transmitter, transmitter controller and data acquisition system used for this work are the same one used for TEM based geophysical applications. The system has been used for the detection of an artificially engineered defect in an aluminum plate at a depth of 2 mm as well as 20 mm below the surface.     

Automatic defect inspection system of colour filters using Taguchi-based neural network

A coloured filter is a critical part of an LCD panel, especially to present a high quality colour display. At present, the defect detection of colour filters is conducted by manual inspection in the final product stage. However, poor detection efficiency and subjective judgment of manual inspection undermine accuracy. Therefore, this study applied image processing technology and the neural network to detect surface defects of colour filters in order to prevent losses arising from incorrect detection, lower production costs, and effectively improve yield. A back-propagation neural network (BPNN) classifier was selected to train the features. The results showed that the proposed method can be successfully applied in defect detection of colour filters to reduce artificial detection errors. In addition, the Taguchi method was used with BPNN to save time searching optimal learning parameters by the trial and error method, which achieves faster convergence, smaller convergent errors and better recognition rate. The results proved that the root-mean-square error (RMSE) of the Taguchi-based BPNN at final convergence is 0.000254, and recognition rate reaches 94%. Therefore, the proposed method has good effects in detecting the micro defects of a colour filter panel.     

基于机器视觉的编织袋表面缺陷检测系统设计与实现

目的 针对编织袋生产中表面缺陷检测效率和精度低等问题,应用机器视觉技术于编织袋表面缺陷检测,进而提高编织袋的生产效率。方法 基于机器视觉设计编织袋表面缺陷检测系统:首先为了降低背景灰度变化对缺陷检测的影响,研究一种同时具有噪声滤除与图像增强功能的预处理算法;其次选取二维最大熵值法对预处理后的编织袋图进行分割,并采用改进遗传算法对它进行优化以增强算法的收敛速度和效果;然后利用特征提取结合形态学处理的方法实现了编织袋表面缺陷的识别与分类;最后应用连通域进行分析,对分类出的缺陷进行统计与定位以获取缺陷的尺寸以及位置信息。结果 采集了200个编织袋缺陷样本,采用文中编织袋表面缺陷检测系统对编织袋样本进行缺陷识别,平均识别准确率为94.0%,处理一幅编织袋图像的时间约为600 ms。结论 该系统具有较高的识别效率和正确率,可实现编织袋表面缺陷的快速检测,满足工业生产的需求。     

一种快速检测光滑半球表面缺陷的方法

针对具有镜面反射特性的光滑球面,提出了一种利用反光带的镜像变形来对表面缺陷进行快速检测的新方法。这种方法通过漫反射反光带增强图像上的缺陷信息,克服了背景噪声在图像处理中会造成较大误差的缺点,也降低了照明系统的设计难度。根据缺陷引起的反光带镜像变形获取缺陷特征,使图像处理软件的工作量大大减少。通过实验,给出了缺陷尺寸和扫描间隔等因素对检测结果影响的分布曲线,为合理提取缺陷尺寸提供了参考。实验表明,该方法的鲁棒性好、效率高,能识别0.5mm以上的全部缺陷,定位误差在 1之内。     

GPU-based real-time small displacement estimation with ultrasound

General purpose computing on graphics processing units (GPUs) has been previously shown to speed up computationally intensive data processing and image reconstruction algorithms for computed tomography (CT), magnetic resonance (MR), and ultrasound images. Although some algorithms in ultrasound have been converted to GPU processing, many investigative ultrasound research systems still use serial processing on a single CPU. One such ultrasound modality is acoustic radiation force impulse (ARFI) imaging, which investigates the mechanical properties of soft tissue. Traditionally, the raw data are processed offline to estimate the displacement of the tissue after the application of radiation force. It is highly advantageous to process the data in real-time to assess their quality and make modifications during a study. In this paper, we present algorithms for efficient GPU parallel processing of two widely used tools in ultrasound: cubic spline interpolation and Loupas' two-dimensional autocorrelator for displacement estimation. It is shown that a commercially available graphics card can be used for these computations, achieving speed increases up to 40x compared with single CPU processing. Thus, we conclude that the GPU-based data processing approach facilitates real-time (i.e., <1 second) display of ARFI data and is a promising approach for ultrasonic research systems.     

Automated Quality Characterization for Composites Using Hybrid Ultrasonic Imaging Techniques

An enhanced technique using image processing has been developed for automated ultrasonic inspection of composite materials, such as glass/carbon-fibre-reinforced polymer (GFRP or CFRP), to ascertain their structural healthiness. The proposed technique is capable of identifying the abnormality features buried in the composite by image filtering and segmentation applied to ultrasonic C-Scan images. This work presents results performed on two composite samples with simulated delamination defects. A local gating scheme is applied to raw A-Scan data for improved contrast between defective and healthy regions in the produced C-Scan image. In this test campaign, different filtering and thresholding algorithms are evaluated and compared in terms of their effectiveness on defect identification. The accuracies of less than 3 mm and 1.11 mm were attained for the defect size and depth, respectively. The results demonstrates the applicability of the proposed technique for accurate defect localization and characterization of composite materials.