A fast and adaptive method for automatic weld defect detection in various real-time X-ray imaging systems

A first and effective method is proposed to detect weld deject adaptively in various Dypes of real-time X-ray images obtained in different conditions. After weld extraction and noise reduction, a proper template of median filter is used to estimate the weld background. After the weld background is subtracted from the original image, an adaptite threshold segmentation algorithm is proposed to obtain the binary image, and then the morphological close and open operation, labeling algorithm and fids＇e alarm eliminating algorithm are applied to pracess the binary image to obtain the defect, ct detection result. At last, a fast realization procedure jbr proposed method is developed. The proposed method is tested in real-time X-ray image,s obtairted in different X-ray imaging sutems. Experiment results show that the proposed method is effective to detect low contrast weld dejects with few .false alarms and is adaptive to various types of real-time X-ray imaging systems.

BACKGROUND RECTIFICATION AND FEATURE EXTRACTION OF IMAGE IN A SPOT WELD OF AL ALLOY X-RAY DETECTION

A primary study on Processing in X - ray inspection of spot weld for aluminum alloy spot welding,in- cluding for background simulation,acquisition of ideal binary image, and extraction and identifi- cation of defect features was presented.

X-ray detection based on complementary metal-oxide-semiconductor sensors

Complementary metal-oxide-semiconductor(CMOS) sensors can convert X-rays into detectable signals; therefore, they are powerful tools in X-ray detection applications. Herein, we explore the physics behind X-ray detection performed using CMOS sensors. X-ray measurements were obtained using a simulated positioner based on a CMOS sensor, while the X-ray energy was modified by changing the voltage, current, and radiation time. A monitoring control unit collected video data of the detected X-rays. The video images were framed and filtered to detect the effective pixel points(radiation spots).The histograms of the images prove there is a linear relationship between the pixel points and X-ray energy. The relationships between the image pixel points, voltage, and current were quantified, and the resultant correlations were observed to obey some physical laws.

Application of Dual-Energy X-Ray Image Detection of Dangerous Goods Based on YOLOv7

X-ray security equipment is currently a more commonly used dangerous goods detection tool, due to the increasing security work tasks, the use of target detection technology to assist security personnel to carry out work has become an inevitable trend. With the development of deep learning, object detection technology is becoming more and more mature, and object detection framework based on convolutional neural networks has been widely used in industrial, medical and military fields. In order to improve the efficiency of security staff, reduce the risk of dangerous goods missed detection. Based on the data collected in X-ray security equipment, this paper uses a method of inserting dangerous goods into an empty package to balance all kinds of dangerous goods data and expand the data set. The high-low energy images are combined using the high-low energy feature fusion method. Finally, the dangerous goods target detection technology based on the YOLOv7 model is used for model training. After the introduction of the above method, the detection accuracy is improved by 6% compared with the direct use of the original data set for detection, and the speed is 93FPS, which can meet the requirements of the online security system, greatly improve the work efficiency of security personnel, and eliminate the security risks caused by missed detection.

Molecular detection of Xanthomonas oryzae pv.oryzae, Xanthomonas oryzae pv. oryzicola, and Burkholderia glumae in infected rice seeds and leaves

The polymerase chain reaction(PCR) is particularly useful for plant pathogen detection. In the present study, multiplex PCR and SYBR Green real-time PCR were developed to facilitate the simultaneous detection of three important rice pathogens, Xanthomonas oryzae pv.oryzae, X. oryzae pv. oryzicola, and Burkholderia glumae. The unique PCR primer sets were designed from portions of a putative glycosyltransferase gene of X. oryzae pv. oryzae, an Avr Rxo gene of X. oryzae pv. oryzicola, and an internal transcribed spacer(ITS) sequence of B. glumae. Using a multiplex PCR assay, X. oryzae pv. oryzae, X. oryzae pv. oryzicola, and B. glumae were detected in one PCR reaction that contained the newly developed primer set mix. Using SYBR Green real-time PCR assays, X. oryzae pv. oryzae, X. oryzae pv. oryzicola, and B. glumae were detected at 1, 1, and 10 fg μL-1, respectively. These newly designed molecular assays are sensitive and could be reliable tools for pathogen detection and disease forecasting.

Readout electronics for a high-resolution soft X-ray spectrometer based on silicon drift detector

The readout electronics for a prototype soft X-ray spectrometer based on silicon drift detector(SDD),for precisely measuring the energy and arrival time of X-ray photons is presented in this paper.The system mainly consists of two parts,i.e.,an analog electronics section(including a pre-amplifier,a signal shaper and filter,a constant fraction timing circuit,and a peak hold circuit)and a digital electronics section(including an ADC and a TDC).Test results with X-ray sources show that an energy dynamic range of 1-10 keV with an integral nonlinearity of less than 0.1%can be achieved,and the energy resolution is better than 160 eV @ 5.9 keV FWHM.Using a waveform generator,test results also indicate that time resolution of the electronics system is about 3.7 ns,which is much less than the transit time spread of SDD(<100 ns)and satisfies the requirements of future applications.

Nested grazing incidence optics for x ray detection

Grazing incidence optics （GIO） is the most important compound in an x-ray detection system; it is used to concentrate the x-ray photons from outer space. A nested planar GIO for x-ray concentration is designed and developed by authors in this paper; planar segments are used as the reflection mirror instead of curved segments because of the simple process and low cost. After the complex assembling process with a special metal supporter, a final circle light spot of φ 12 mm was obtained in the visible light testing experiment of GIO; the effective area of 1710.51 mm^2@ 1 keV and 530 mm^2@8 keV is obtained in the x-ray testing experiment with the GIO-SDD combination, which is supposed to be a concentrating detector in xray detection systems.

Detection technology of micro residual core in small inner cavity of complex castings

A new technology for detecting a tiny residual core in the small inner cavity of complex castings is proposed. The residual core is identified by using image recognition technology. Tracer processing and image signal processing are combined to enhance the image contrast. The relationships between the concentration of tracer, the size of the residual core, the wall thickness of the castings and the contrast were obtained. Based on the experimental data, the minimum detectable amount of residual core under different conditions was obtained. The results show that the minimum detectable amount decreases from 4.398 mg to 0.438 mg for the 1.0 mm wall thickness casting when the concentration of tracer increases from 0% to 20%. The signal-to-noise ratio(SNR) of the detection results increases by 27.010 by means of average filtering and linear point operation. The subtraction of image and image background was performed, and then the boundary extraction was carried out to obtain a clear and reliable result. The experimental results show that the non-traced residual core cannot be detected for a blade with a thickness less than 5 mm. The residual core of 1 mm thickness can be barely identified by artificial recognition after tracer processing and image processing, while the residual core of 0.6 mm thickness can be detected clearly using image recognition technology.

Direct Conversion X-Ray Detectors with High Sensitivity at Low Dose Rate Based on All-Inorganic Lead-Free Perovskite Wafers

Just as lead-based perovskites that are hot in solar cell preparation, Bi-based perovskites have demonstrated excellent performance in direct X-ray detection, especially the Cs3Bi2I9 single crystals (SCs). However, compared with lead-halide perovskites, one challenge for the Cs3Bi2I9 SCs for X-ray detection application is that it is difficult to prepare large-sized and high-quality SCs. Therefore, how to get a large area with a high-quality wafer is also as important as Cs3Bi2I9 growth method research. Here, different anti-solvents are used for the preparation of poly-crystalline powder with the Antisolvents precipitation (A) method, as a control, High-energy ball milling (B) was also used to prepare poly-crystalline powders. The resultant two types of Cs3Bi2I9 wafer exhibit a micro-strain of 1.21 × 10-3, a resistivity of 5.13 × 108 Ω cm and a microstrain of 1.21 × 10-3, a resistivity of 2.21 × 109 Ω cm. As a result, an X-ray detector based on the high-quality Cs3Bi2I9 wafer exhibits excellent dose rate linearity, a sensitivity of 588 μC·Gyairs-1·cm-2 and a limit of detection (LoD) of 76 nGyair·s-1.

基于探针光调制的皮秒分辨X-ray探测方法与实验

高能密度物理研究中涉及许多单次皮秒现象的诊断测量,然而对单次X-ray脉冲形状、X-ray与激光脉冲的皮秒精度同步依然是极具挑战的课题.传统行波选通分幅相机受电子渡越时间限制,难以突破40 ps时间分辨极限.本文围绕半导体中光学探针光的全光调制效应,提出一种以低温GaAs材料为基础,实现皮秒时间分辨X-ray探测的新方法,详细阐述了该探测器的工作机理、器件参数设计和时间分辨能力.通过飞秒激光打靶实验,验证了其概念设计的正确性.结果表明该探测器具有约1.5 ps时间响应和10 ps时间分辨能力,通过材料优化可将时间分辨提升至1 ps以内.

基于X-ray和RGB图像融合的实蝇侵染柑橘无损检测

实蝇侵染柑橘流入市场会造成巨大的经济损失,因此需要在商品化处理阶段对其全面筛除。针对柑橘在实蝇侵染早期没有明显外部特征,人工抽样检测效率低、筛除难的问题,探索了在生产线上同时搭载农业X光机与RGB相机进行无损检测的可行性,提出了基于X-ray(X光)和RGB图像的多模态数据融合方法,建立了CNN-LSTM检测模型,实现了实蝇侵染柑橘高精度无损检测。模拟了柑橘在生产线上滚动并被拍摄6幅X-ray和RGB序列图像的过程,构建了实蝇侵染柑橘的多源数据集,融合了不同模态的实蝇侵染特征信息,提升了实蝇侵染柑橘检测模型的检测能力,并对比了ResNet18-LSTM、GoogleNet-LSTM、SqueezeNet-LSTM、MobileNetV2-LSTM轻量化检测模型,验证了多模态数据融合方法的有效性。研究结果表明,提出的多模态数据融合实蝇侵染柑橘方法比单模态检测方法检测性能更加优异,其中ResNet18-LSTM检测准确率最高,多模态的图像融合和特征融合方法检测准确率分别达到97.3%和95.7%,单模态X-ray和RGB检测方法准确率分别为93.2%和89.3%。本研究可为实蝇侵染柑橘在线无损检测技术与装备的研究提供理论支撑。

微米级X-ray断层扫描技术在TiAl铸件无损检测中的应用

研究采用MU2000 X射线仪对Ti6Al4V合金的铸造工艺进行了研究.通过逐级放大倍率法对Ti6Al4V合金铸件的内部进行微观检测,X射线断层成像图片表明:铸件内部大部分区域呈现灰色,该灰色部位代表着铸件致密的结构,证明浇注工艺及型壳结构设计的合理性;同时,还观察到铸件内部存在气孔、缩孔和熔接痕缺陷.经分析认为:浇注温度、型壳预热温度偏低是导致产生气孔、缩孔缺陷的主要原因,而熔接痕缺陷则与浇注系统设计的不合理相关.

基于RetinaNet-AACIDD的铝合金铸件X-ray图像缺陷检测方法

为了解决铝合金铸件X射线图像噪声多、缺陷背景复杂,缺陷较难检测的问题,制作了一个含有14640张准确标注的大型铝合金铸件内部X射线图像缺陷数据集ALU-Xray,提出一种基于深度学习的缺陷检测算法RetinaNet-AACIDD(RetinaNet for aluminum alloy casting internal defect detection)。通过加入由通道注意力模块C-Block和空间注意力模块S-Block组成的混合注意力模块CS-Block,有效降低了噪声、背景等无关信息对检测精度的干扰,并通过多尺度特征融合模块,完成了深层语义信息的向下传递,结合多尺度特征预测和基于改进的聚类算法重新设计的锚框,使得模型对气泡、裂纹等尺度较小的缺陷和复杂背景下的疏松、缩孔等缺陷的检测精度显著提升。

双色散-双聚焦X射线光谱仪研制与特性研究

X射线发射谱技术(XES)是一种原位无损获得元素化学形态的技术方法,目前国内外实验室型X射线发射谱测定装置的研制尚处于探索阶段。能量色散X射线荧光(EDXRF)光谱仪和波长色散X射线荧光(WDXRF)光谱仪在地质、环境、考古等多学科领域得到广泛应用,然而,EDXRF虽结构简单,可实现多元素快速无损检测,但分辨率不理想、谱线干扰严重,检出限较差;WDXRF虽可分辨常规应用中多数具有谱线重叠特性的元素,但结构复杂成本高。为探索实验室型XES装置研制,该研究综合了能量色散X射线荧光和波长色散X射线荧光的性能优势,提出了一种波长-能量双色散、激发-发射双聚焦X射线光谱仪(DDF-XRS)设计理念,并成功研制了原理样机。实验数据和分析结果显示,该DDF-XRS型光谱仪兼具聚焦X射线和波长与能量色散光谱仪的优点,结构简单,信噪比好,具有高分辨率和低检出限特性。通过波长-能量双色散技术,X射线首先被晶体衍射发生波长色散,从而获得单色光,同时利用硅漂移探测器的能量色散特性,降低谱线干扰误判风险,提高分析结果准确,该技术克服了WDXRF结构复杂和EDXRF能量分辨率不足的限制,凸显了双色散的必要性和优越性。目前DDF-XRS光谱仪分辨率45 eV,可减少过渡金属K_(β)对K_(α)峰的谱线重叠影响;同时,显著降低了连续谱背景,最优信噪比>1000;测定地质样品中的Cr,检出限可达0.26 mg·kg^(-1)。应用DDF-XRS,一定程度上已可分辨过渡金属K_(α1)和K_(α2)谱线,如结合线性或二维阵列探测器,则有望进一步提高分辨率,实现X射线发射指纹谱的测定,以获得分析元素的化学形态。由于目前的晶体特性尚不能达到完全分辨过渡金属的重叠谱线的性能要求,故探寻具有高分率能力、高衍射强度特性的弯晶,将成为下一步的研究重点。

硅像素探测器X射线探测效率实验研究

X射线探测器经历了气体探测器和闪烁体探测器时代,进入了半导体探测器时代。作为一种新型的半导体探测器,硅像素探测器具有分辨率好、探测效率高、时间响应快、功耗低等特点。为了研究硅像素探测器X射线探测效率,设计了一套X射线探测系统,并进行了实验研究。实验分别测试了4.51、5.41、6.40、8.05 keV共4种不同能量的X射线的探测效率。后期进行了误差分析和数据处理,得到4种不同能量X射线正面入射时探测效率分别为53.00%、51.56%、40.65%和29.91%。该实验研究为寻找高探测效率的X射线探测器提供了一种新的思路。

基于变换矩阵的BGA X-ray图像倾斜识别及校正方法

针对基于X-Ray的焊球阵列封装(BGA)焊接缺陷检测中,现有的图像处理算法无法有效处理PCB意外倾斜的问题,提出了一种自动判断BGA焊点X射线图像倾斜程度并进行校正的方法。首先根据BGA焊点阵列排布的特性恢复出原始图像中的结构信息,然后使用广义逆矩阵求解出理想正视图像与实际倾斜图像之间的变换矩阵,最后对倾斜图像进行逆变换以得到理想正视图像,并使用变换矩阵估计出PCB的倾斜程度。实验针对正视与倾斜的BGA焊点的X射线图像,运用所提方法,准确判别出图像的倾斜程度,并进行了校正。此方法既可以作为一种图像质量评价算法,在BGA X-Ray缺陷检测中判断图像是否为需要的正视图像;也可以作为一种图像自动校正算法,提升基于X射线的自动检测系统的适应性。

基于神经网络架构搜索的X射线图像违禁品检测算法

为了提高卷积神经网络设计的自动化程度并进一步提高复杂背景下违禁品检测的准确率和速度,提出了一种基于神经网络架构搜索的X射线图像违禁品检测算法。首先,设计逐层渐进式搜索策略和多分支搜索空间,并基于批量归一化指标为每一个layer结构搜索最佳侧分支;然后,逐层搜索构建新的骨干网络组件;最后,组成由数据驱动的新目标检测模型。该算法在数据集HiXray、OPIXray、PIDray上分别取得了83.4%、87.2%、70.4%的检测精度。实验结果表明,本文算法能够自适应数据集并自动搜索出性能更好的Backbone组件,与FCOS、YOLOv4等主流算法相比,有效提高了复杂背景下违禁品检测的准确率和速度。

基于便携式X射线荧光光谱速测的设施菜地土壤重金属污染诊断与评价

土壤重金属污染快速诊断与评价是污染防控与风险管理的前提。以广东省典型设施蔬菜产地土壤为研究对象,采用便携式X射线荧光光谱法(portable X-ray fluorescence spectroscopy,PXRF)原位快速获取土壤重金属(镉(Cd)、铜(Cu)、铬(Cr)、铅(Pb)、锌(Zn)、砷(As)、镍(Ni)及汞(Hg))含量,在传统实验室方法验证PXRF的准确性和稳定性的基础上,对广东省设施菜地表层土壤重金属的污染状况进行快速诊断,并对土壤重金属污染风险进行评价。结果表明:(1)异位PXRF法对土壤Cd、Cu、Cr、Pb、Zn、As及Ni的测定值与传统实验室方法测定值极显著相关(P<0.01),土壤Pb和As含量的PXRF原位测定值与传统实验室方法测定值极显著相关(P<0.01);(2)土壤含水量为150 g·kg^(-1)~200 g·kg^(-1)时,Cr、Pb、Zn和As的PXRF原位测定值与实验室测定值的相关性达到了极显著水平(P<0.01);(3)土壤Cd和Cu的超标现象较为突出,其点位超标率分别为20.9%和10.0%。设施菜地土壤Cd的平均含量为0.21 mg·kg^(-1),约为露天农田土壤的1.2倍。珠江三角洲地区设施土壤中各重金属的含量整体较其他地区高;(4)土壤Cd、Cu、Cr、Pb、Zn、As、Ni及Hg的单项污染指数均小于1,由高到低依次为Cd>Cu>Pb>Zn>Cr>As>Ni>Hg,内梅罗综合污染指数的均值为0.69。就整体平均值而言,土壤重金属污染程度处于清洁水平。PXRF实现了广东省设施菜地土壤重金属污染的快速诊断,并快速识别出Cd和Cu为设施菜地污染风险较高的潜在污染元素,研究结果可为设施农业土壤重金属污染快速诊断与评价提供方法和数据支撑。

自适应与多尺度特征融合的X光违禁品检测

针对X光安检违禁品图像空间多尺度变化、背景干扰及模型复杂等问题,提出了空间自适应与多尺度特征融合的YOLOv5轻量模型。以YOLOv5为基本框架,引入自适应空间特征融合机制抑制特征尺度差异的影响,结合双向特征金字塔网络集成了特征双向加权融合;采用轻量化通道注意力机制获得编码的位置信息,增强有效特征的表达;同时利用GhostConv替换部分Conv降低网络计算复杂度。此模型在OPIXray、SIXray、HiXray等3个公开数据集上mAP分别达到94.2%、92.8%、83.3%,比基线模型分别提高了5.4、0.5、1.7个百分点,且未明显改变推理效率,较好兼顾了模型检测精度与速度,优于当前诸多先进算法。