Imaging Of Defects Research Articles

Multi-scale ultrasonic imaging of sub-surface concrete defects

This paper introduces a new method for creating the ultrasonic image of concrete that utilizes both linear and nonlinear wave properties in a single measurement. Linear ultrasonic imaging relies on wave velocity. The resolution of linear ultrasonics for various inclusion sizes and inclusion-to-base material ratios is numerically and experimentally studied. The nonlinear ultrasonic testing (NLUT) image is based on the acoustic nonlinearity coefficient, calculated using inputs of the first and second harmonic amplitudes, wave number, and the distance between the transmitter and receiver. The ultrasonic waveform is decomposed into its harmonics using wavelet transform. Their wave velocities, wave numbers, and amplitudes are extracted to calculate the linear wave velocity and the acoustic nonlinearity coefficient. Four calibration samples with different inclusions are tested to evaluate the effectiveness of combining two imaging methods: pure concrete, concrete with small foam inserts simulating air inclusions, and two concrete samples with larger foam inclusions. The angle dependence of acoustic nonlinearity coefficient is shown. The results show that constructing both linear and nonlinear ultrasonic imaging is necessary when the defect’s properties are unknown.

Imaging Voids and Defects Inside Li-Ion Cathode LiNi0.6Mn0.2Co0.2O2 Single Crystals.

Li-ion battery cathode active materials obtained from different sources or preparation methods often exhibit broadly divergent performance and stability despite no obvious differences in morphology, purity, and crystallinity. We show how state-of-the-art, commercial, nominally single crystalline LiNi0.6Mn0.2Co0.2O2 (NMC-622) particles possess extensive internal nanostructure even in the pristine state. Scanning X-ray diffraction microscopy reveals the presence of interlayer strain gradients, and crystal bending is attributed to oxygen vacancies. Phase contrast X-ray nano-tomography reveals two different kinds of particles, welded/aggregated, and single crystal like, and emphasizes the intra- and interparticle heterogeneities from the nano- to the microscale. It also detects within the imaging resolution (100 nm) substantial quantities of nanovoids hidden inside the bulk of two-thirds of the overall studied particles (around 3000), with an average value of 12.5%v per particle and a mean size of 148 nm. The powerful combination of both techniques helps prescreening and quantifying the defective nature of cathode material and thus anticipating their performance in electrode assembly/battery testing.

Quantitative test of delamination defects in CFRP with surface interference by laser thermography

Remote onsite inspection and quantitative evaluation of internal delamination defects in carbon fiber reinforced polymer (CFRP) structures is of great significance in practical engineering applications. In this paper, a new method for quantitative test and 3D imaging of internal delamination defects in CFRP with surface interference by long pulse laser thermography is developed. The variation of surface temperature field in a CFRP plate with both defect and surface interference irradiated by a uniform laser source was analyzed by simulation and experiment. Image processing of the collected laser thermography image sequence was carried out to obtain the size and shape of the possible defects. Meanwhile, a method with using the peak time of the temperature difference curve was developed to precisely evaluate the defect depth and eliminate the surface interference. The information was then combined to achieve 3D visualization imaging of the actual defects.

Investigation on local monitoring paradigms of in-situ conformally fabricated piezopolymer coating-based array transducers: Ultrasonic bulk waves and local ultrasonic resonances

Realistic structures with complex features have been intricate challenges for structural health monitoring (SHM) with permanently installed transducers. Poor conformability of conventional rigid and brittle piezoceramic wafers is a typical issue of applications on surfaces with complex geometry. Moreover, the accompanied high-stress concentration requires high-sensitivity defect detection which is difficult to achieve with large-area monitoring methods like lamb waves at tens to hundreds kHz. Previously, in-situ conformally fabricated piezopolymer coating-based array transducers (PCATs) have been developed to build large-area, lightweight, flexible, and tunable Lamb wave networks. In this study, two novel local monitoring methods were investigated with PCATs, namely ultrasonic bulk wave array inspection and local ultrasonic resonance spectroscopy. For thick structures, ultrasonic bulk waves were generated and detected by PCATs with broadband operating frequencies (1–10 MHz) and flexible array parameters. Simulation tools and imaging algorithms of array inspection in non-destructive testing (NDT) were well implemented based on the analogous directivity pattern and normal-pressure coupling mechanism. As proof of concept, PCATs were applied on example structures with flat, concave, and convex surfaces for internal defect imaging. For thin-walled and/or multilayer structures, PCATs were used to measure local ultrasonic resonances, comparable to conventional non-contact methods. With negligible influence on local mechanical properties and broadband frequency response, multiple resonance peaks from 0 to 25 MHz were identified as zero group velocity (ZGV) Lamb modes and thickness vibration modes of host structures, which can be used as damage indices for local monitoring of corrosion, delamination, stiffness degradation, etc. Through embracing advanced NDT techniques with PCATs, high-sensitivity and quantitative local monitoring could be achieved with conformal networks, offering the possibility to integrate with large-area monitoring as multi-scale SHM for complex structures.

Advanced Immunolabeling Method for Optical Volumetric Imaging Reveals Dystrophic Neurites of Dopaminergic Neurons in Alzheimer’s Disease Mouse Brain

Optical brain clearing combined with immunolabeling is valuable for analyzing molecular tissue structures, including complex synaptic connectivity. However, the presence of aberrant lipid deposition due to aging and brain disorders poses a challenge for achieving antibody penetration throughout the entire brain volume. Herein, we present an efficient brain-wide immunolabeling method, the immuno-active clearing technique (iACT). The treatment of brain tissues with a zwitterionic detergent, specifically SB3-12, significantly enhanced tissue permeability by effectively mitigating lipid barriers. Notably, Quadrol treatment further refines the methodology by effectively eliminating residual detergents from cleared brain tissues, subsequently amplifying volumetric fluorescence signals. Employing iACT, we uncover disrupted axonal projections within the mesolimbic dopaminergic (DA) circuits in 5xFAD mice. Subsequent characterization of DA neural circuits in 5xFAD mice revealed proximal axonal swelling and misrouting of distal axonal compartments in proximity to amyloid-beta plaques. Importantly, these structural anomalies in DA axons correlate with a marked reduction in DA release within the nucleus accumbens. Collectively, our findings highlight the efficacy of optical volumetric imaging with iACT in resolving intricate structural alterations in deep brain neural circuits. Furthermore, we unveil the compromised integrity of DA pathways, contributing to the underlying neuropathology of Alzheimer’s disease. The iACT technique thus holds significant promise as a valuable asset for advancing our understanding of complex neurodegenerative disorders and may pave the way for targeted therapeutic interventions.Graphical The axonal projection of DA neurons in the septum and the NAc showed dystrophic phenotypes such as growth cone-like enlargement of the axonal terminus and aggregated neurites. Brain-wide imaging of structural defects in the neural circuits was facilitated with brain clearing and antibody penetration assisted with SB3-12 and Quadrol pre-treatment. The whole volumetric imaging process could be completed in a week with the robust iACT method. Created with https://www.biorender.com/.

Cross-Correlation Inspired Residual Network for Pulsed Eddy Current Imaging and Detecting of Subsurface Defects

The decay of pulsed eddy currents (PEC) with depth in diffusion decreases resultant signal changes, thus bringing a challenge for reliable and accurate evaluation of deep subsurface defects. In this work, a novel cross-correlation inspired residual network, termed CCResNet, is proposed to improve the capability for smart evaluation of subsurface defects. It consists of a cross-correlation layer, a residual network, and a novel loss function, namely, focal-probability of detection (Focal-POD) loss. The customized Gaussian wavelet basis enables us to derive weak features from heavily noised PEC signals due to the similarity by cross-correlation operation, which is the origin of the constructed cross-correlation layer. Then, a Focal-POD loss is proposed to address class imbalance and endow CCResNet with powerful capability for detection of deep subsurface defects by increasing their loss values. Finally, a semi-supervised framework is built to re-train CCResNet using pseudo and labelled dataset to obtain classified results as imaging features. The experimental results show that the developed CCResNet is featured as better imaging resolution, more accurate evaluation, and intelligence in detection of deeper subsurface defects.

Chronic thromboembolic pulmonary disease: Association with exercise-induced pulmonary hypertension and right ventricle adaptation over time

Background and AimChronic thromboembolic pulmonary disease (CTEPD) is a progressive condition caused by fibrotic thrombi and vascular remodeling in the pulmonary circulation despite prolonged anticoagulation. We evaluated clinical factors associated with CTEPD, as well as its impact on functional capacity, pulmonary haemodynamics at rest and after exercise, and right ventricle (RV) morphology and function. MethodsWe compared 33 consecutive patients with a history of acute pulmonary embolism and either normal pulmonary vascular imaging (negative Q-scan, group 1, n = 16) or persistent defects on lung perfusion scan (positive Q-scan) despite oral anticoagulation at 4 months (group 2, n = 17). Investigations included thrombotic load, the Pulmonary Embolism Severity Index (PESI) score, functional class, N-terminal prohormone of brain natriuretic peptide (NT-proBNP), cardiopulmonary exercise test (CPET) and echocardiographic parameters at rest and after exercise (ESE), at 4 and at 24 months. ResultsCompared with group 1, group 2 featured a higher PESI score (p = 0.02) and a higher thrombotic load (p = 0.004) at hospital admission. At 4 months, group 2 developed exercise-induced pulmonary hypertension (Ex-PH) at CPET (p < 0.001) and ESE (p < 0.001). At 24 months group 2 showed higher NT-proBNP (p < 0.001), WHO-FC (p < 0.001), systolic (p<0.001) and diastolic (p = 0.037) RV dysfunction and worse RV-arterial coupling (p < 0.001) despite maintaining a low or intermediate echocardiographic probability of PH. ConclusionsThis is the first "proof of concept" study showing that patients with a positive Q-scan frequently develop Ex-PH and RV functional deterioration as well as reduced functional capacity, generating the hypothesis that Ex-PH could help detect the progression to CTEPD.

Structure optimization of heterogeneous compound eye camera for improving the detection performance

To achieve fast location, precise tracking and accurate identification over a large field of view (FOV), we have proposed a heterogeneous compound eye camera (HeCECam), which consists of a heterogeneous compound eye array, an optical relay system and a CMOS detector. However, the current HeCECam can hardly acquire high-precision 3D information of the targets to realize these applications. To solve this challenge, we propose a scheme on optimizing the structure of the HeCECam to improving the detection performance, including the optimization of the distribution uniformity of the sub-eyes with the proposed “Three-direction center-of-gravity subdivision (TGS)” and the enhancement of the compatibility between heterogeneous compound eyes and the optical relay system with the proposed compensation method for tilt. The TGS significantly reduces the distribution unevenness of sub-eyes down to 117% from the previous 152%, and provides symmetry to the heterogeneous compound eye array. The tilt compensation effectively addresses previous imaging defects, such as distortion of sub-images, increased stray light, and support structures being imaged, and it improves the imaging clarity of the system, especially in external FOV. Based on two proposed methods, we re-design and fabricate the heterogeneous compound eye array to obtain a high-performance prototype. To verify the imaging capacities of the optimized HeCECam, a series of comparison experiments are performed, including blank scene imaging, FOV tests, resolution verification and real-world scene imaging. The results show that the previous imaging defects have been well eliminated, and the optimized prototype has stronger resolving power and wider FOV. This allow the HeCECam to perform better in subsequent practical applications, such as wide-area surveillance, forewarning, and navigation.

Low-Resolution Steel Surface Defects Classification Network Based on Autocorrelation Semantic Enhancement

Aiming at the problems of low-resolution steel surface defects imaging, such as defect type confusion, feature blurring, and low classification accuracy, this paper proposes an autocorrelation semantic enhancement network (ASENet) for the classification of steel surface defects. It mainly consists of a backbone network and an autocorrelation semantic enhancement module (ASE), in which the autocorrelation semantic enhancement module consists of three main learnable modules: the CS attention module, the autocorrelation computation module, and the contextual feature awareness module. Specifically, we first use the backbone network to extract the basic features of the image and then use the designed CS attention module to enhance the basic features. In addition, to capture different aspects of semantic objects, we use the autocorrelation module to compute the correlation between neighborhoods and contextualize the basic and augmented features to enhance the recognizability of the features. Experimental results show that our method produces significant results, and the classification accuracy reaches 96.24% on the NEU-CLS-64 dataset. Compared with ViT-B/16, Swin_t, ResNet50, Mobilenet_v3_small, Densenet121, Efficientnet_b2, and baseline, the accuracy is 9.43%, 5.15%, 4.87%, 3.34%, 3.28%, 3.01%, and 2.72% higher, respectively.

Research on ultrasonic defect imaging based on a neural network with Gaussian weight function fusion model

Ultrasonic testing is widely used in the industrial field because of its high sensitivity, safety and low-cost, which plays an increasingly important role in detecting defects on industrial. However, at present, defect detection only stays in scanning surface defects and classifying the internal defects of the specimen. The accuracy of quantitative imaging of the morphology of the internal defects of the specimen is still an issue to be improved. In this paper, the simple defects in several specimens are detected by ultrasound based on finite element simulation software and the accuracy of the signal can be verified by ultrasonic experiment. Then, the features can be extracted by signal processing method. The neural network imagines the inversion results with the Gaussian weight function fusion model. Finally, this paper uses the signals of simple shape defects as the training set to detect the shapes of complex defects and obtain the corresponding imaging results.

Laser ultrasonic imaging of defect in bimetallic media with frequency domain synthetic aperture focusing technology

A laser ultrasonics (LU) based frequency domain synthetic aperture focusing technology (LUB-F-SAFT), which uses 2D equivalent velocity (SEV) and fuses multi-mode ultrasound, is developed to image the internal horizontal hole defects with a diameter of ∼1.0mm in bimetallic composites. Bulk waves non-destructively excited by a line-shaped laser are detected by a vibrometer away from the excitation. The results indicate that the LUB-F-SAFT with SEV can improve the imaging quality and locating capability, and the testing error is about 0.02mm. The improvement in the imaging quality using SEV for different ratios of the first layer thickness to defect-to-interface distances is discussed. In addition, a theoretical approach based on the directionality of the detected ultrasound is applied to determine a reasonable excitation-detection distance for better defect imaging. A LUB-F-SAFT imaging combining multi-mode waves is employed to improve the range of defect profiles. The results show that the range of the imaged defect profile becomes larger using the multi-mode fused LUB-F-SAFT imaging, which is an improvement of about 49.2% compared to the shear wave imaging result.

Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A Review.

This paper provides an in-depth overview of Deep Neural Networks and their application in the segmentation and analysis of lung Magnetic Resonance Imaging (MRI) scans, specifically focusing on hyperpolarized gas MRI and the quantification of lung ventilation defects. An in-depth understanding of Deep Neural Networks is presented, laying the groundwork for the exploration of their use in hyperpolarized gas MRI and the quantification of lung ventilation defects. Five distinct studies are examined, each leveraging unique deep learning architectures and data augmentation techniques to optimize model performance. These studies encompass a range of approaches, including the use of 3D Convolutional Neural Networks, cascaded U-Net models, Generative Adversarial Networks, and nnU-net for hyperpolarized gas MRI segmentation. The findings highlight the potential of deep learning methods in the segmentation and analysis of lung MRI scans, emphasizing the need for consensus on lung ventilation segmentation methods.

Near-surface defect detection in additively manufactured components using laser induced phased arrays with surface acoustic wave crosstalk suppression

In-process inspection of the additive manufacturing process requires a technique that can provide reliable measurements given the extreme operating environments, the small size of the defects and the cyclic melting and heating of the material, caused by subsequently deposited layers. A remote and couplant-free ultrasonic inspection technique using bulk waves that can image near-surface defects could address these in-process inspection requirements. Laser induced phased arrays (LIPA) generate and detect ultrasound based on laser ultrasonics principles, while the array is synthesised in post-processing. However, when using LIPAs for inspection, the surface acoustic waves (SAWs) interfere with the bulk wave modes giving rise to crosstalk and artefacts, which makes near-surface defect imaging difficult. This work experimentally validates and compares five techniques for SAW suppression: amplitude thresholding, mean waveform subtraction, principal component subtraction, frequency-wavenumber filtering, and phase coherence imaging. SAW suppression is demonstrated in ultrasonic images of transverse waves based on 71-element LIPA data synthesised on a Ti-6Al-4V directed energy deposition-arc (DED-Arc/Ti6Al4V) sample with a ∼1 mm diameter side drilled hole, located at ∼4 mm below the inspected surface. The reported results show that the principal component subtraction approach achieved the highest ‘signal-to-crosstalk ratio’ improvement of 16 dB, while successfully suppressing the SAW.

F-SAFT based on laser-induced directional multimode ultrasound for defect imaging

In conventional laser ultrasonic (LU) single-mode frequency domain synthetic aperture focusing technique (F-SAFT) imaging, the precision of the imaged defect profile is limited due to the influence of the directionality of laser-induced ultrasound and the directionality of detection. To solve this problem, LU-based F-SAFT imaging with the shear wave, longitudinal wave, and mode-converted wave is proposed in this article. In the experiment, multimode bulk waves are excited by a pulsed laser, and a laser Doppler vibrometer is employed to detect ultrasound waves. The F-SAFT imaging is performed with single-mode and multimode ultrasound wave signals, respectively. The results show that F-SAFT imaging fusing three mode signals (shear, longitudinal, and mode-converted waves) improves the range of the upper surface of the imaged defect by nearly two times. In addition, the profile of the imaged defect is more complete than that of using a combination of both longitudinal and shear waves. By analyzing the influence of the directionality of laser-induced ultrasound and the detection directionality caused by the out-of-plane ultrasound component on the imaging results, a theoretical approach is used to select a reasonable excitation–detection distance for the use of multimode-fused F-SAFT algorithm. Furthermore, the feasibility of the theoretical approach is verified by the experimental results.

Chronic thromboembolic disease is associated with exercise-induced pulmonary hypertension and functional right ventricle deterioration

Abstract Background and Aim Chronic thromboembolic pulmonary disease (CTED) is a progressive disease caused by a wall-adherent, fibrotic thrombus in the pulmonary circulation persisting despite oral anticoagulation and despite vascular remodeling of the small arteries. Despite normal pulmonary hemodynamics at rest, exercise intolerance and dyspnea have been reported. Drivers and risk factors, as well as the clinical impact of CTED, are not yet adequately characterized. We evaluated clinical factors predictive of the development of postthrombotic pulmonary disease in a cohort of patients with a history of acute pulmonary embolism on oral anticoagulant therapy. We also evaluated its impact on functional capacity, pulmonary haemodinamics at rest and after exercise and right ventricle morphology and function. Methods We compared 33 consecutive patients with a history of acute pulmonary embolism and normal pulmonary vascular imaging (group 1, n=16) or persistent defects on perfusion scan (positive P scan) despite therapeutic anticoagulation for 4 months (group 2, n=17), for the following parameters: 1. Thrombotic load (extension of pulmonary thromboembolism on CT angiography) and Pulmonary Embolism Severity Index (PESI) score at the time of admission to the Intensive Care Unit (ICU); 2. Presence of thrombophilia (factor V Leiden, prothrombin variant, lupus anticoagulant); 3. Cardiovascular risk factors; 4. Anthropometric and demographic parameters; 5. Anticoagulation treatment in ICU and at discharge. The two groups of patients were also compared for the World Health Organization functional class (WHO-FC), NT-proBNP, cardiopulmonary exercise test (CPET) and echocardiographic parameters at rest and after exercise (ESE), at 4 and after 24 months. Results Compared with group 1, patients with persisting perfusion defects (group 2) featured higher thrombotic load (p=0.004) and PESI score (p=0.02) at the time of ICU admission. At 4 months, group 2 developed exercise-induced pulmonary hypertension (Ex-PH) at CPET (p&amp;lt;0.001) and ESE (p&amp;lt;0.001). At the 24 months follow-up group 2 showed higher NT-proBNP (p&amp;lt;0.001) and WHO-FC (p&amp;lt;0.001), systolic (p&amp;lt;0.001) and diastolic (p=0.037) right ventricular (RV) dysfunction and worse echo indices of RV-Arterial Coupling (TAPSE/PAPs (p&amp;lt;0.001)), despite maintaining a low or intermediate echocardiographic probability of PH (Table 1). Conclusions Despite the low and intermediate echocardiographic probability of PH, patients with persistent positive P scan had reduced functional capacity, and developed ExPH and RV functional deterioration.

Ultrasonic guided wave defect detection method for tank bottom plate based on SH0 mode multichannel magnetostrictive sensor

To address the challenge of ultrasonic detection of multiple defects in large-scale tank bottom plates, a multi-channel magnetostrictive sensor was designed and developed to effectively excite SH (Shear Horizontal) mode guided waves. The optimal overall scheme of the sensor was obtained by considering the sensor excitation energy, coupling efficiency, and fabrication process. Through experimental tests, the consistency of each channel of the sensor is evaluated, and the center frequency and detection range of the sensor are measured. To detect defects of varying sizes and depths in steel plates, a rotating total focus imaging method is proposed. And we conducted experiments using a multi-channel magnetostrictive sensor to achieve SH0 guided wave imaging of multiple defects across the full domain. Our experimental results demonstrate that, across 8 rotations, we achieved a minimum relative position error of 4.32% and a minimum absolute angle error of 2.16°. Our research provides an effective solution for ultrasonic detection of tank bottom plates, and the proposed method is also applicable for defect detection in other large plate-like structures.

Research on the 3D Reverse Time Migration Technique for Internal Defects Imaging and Sensor Settings of Pressure Pipelines.

Although pressure pipelines serve as a secure and energy-efficient means of transporting oil, gas, and chemicals, they are susceptible to fatigue cracks over extended periods of cyclic loading due to the challenging operational conditions. Their quality and efficiency directly affect the safe operation of the project. Therefore, a thorough and precise characterization approach towards pressure pipelines can proactively mitigate safety risks and yield substantial economic and societal benefits. At present, the current mainstream 2D ultrasound imaging technology faces challenges in fully visualizing the internal defects and topography of pressure pipelines. Reverse time migration (RTM), widely employed in geophysical exploration, has the capability to visualize intricate geological structures. In this paper, we introduced the RTM into the realm of ultrasonic non-destructive testing, and proposed a 3D ultrasonic RTM imaging method for internal defects and sensor settings of pressure pipelines. To accurately simulate the extrapolation of wave field in 3D pressure pipelines, we set the absorbing boundary and double free boundary in cylindrical coordinates. Subsequently, using the 3D ultrasonic RTM approach, we attained higher-precision 3D imaging of internal defects in the pressure pipelines through suppressing imaging artifacts. By comparing and analyzing the imaging results of different sensor settings, the design of the observation system is optimized to provide a basis for the imaging and interpretation of actual data. Both simulations and actual field data demonstrate that our approach delivers top-notch 3D imaging of pipeline defects (with an imaging range accuracy up to 97.85%). This method takes into consideration the complexities of multiple scattering and mode conversions occurring at the base of the defects as well as the optimal sensor settings.

Automated quantification of small defects in ultrasonic phased array imaging using AWGA-gcForest algorithm

ABSTRACT The imaging quality of ultrasonic phased array technology significantly affects the quantification of small defects inside metals. Moreover, traditional manual evaluation methods require a substantial amount of manpower and time, and they are easily influenced by subjective factors. To mitigate the impact of imaging quality on the automated quantification of defects, this study proposes an improved gcForest algorithm (AWGA-gcForest, Adaptive window genetic algorithm- gcForest). Firstly, the acquired ultrasonic full matrix data from the phased array is processed using full focusing A-scan technique. Additionally, a window averaging weighting method is introduced for ultrasound signal processing to eliminate the interference from incident waves and backwall echoes. Subsequently, a multi-scale adaptive sliding window adjustment strategy based on Mean Absolute Deviation (MAD) is employed to reduce the computational time complexity of the gcForest algorithm. Furthermore, a weighted approach optimised with a genetic algorithm is utilised in the cascade forest to improve the classification accuracy and generalisation capability. Experimental results demonstrate that the proposed method achieves an accuracy of 97.50%, precision of 97.26%, recall rate of 96.63%, and F1 score of 96.92, exhibiting higher detection accuracy compared to other models while also reducing time costs.

LGP-YOLO: an efficient convolutional neural network for surface defect detection of light guide plate

Light guide plate (LGP) is a key component of liquid crystal display (LCD) display systems, so its quality directly affects the display effect of LCD. However, LGPs have complex background texture, low contrast, varying defect size and numerous defect types, which makes realizing efficient and accuracy-satisfactory surface defect automatic detection of LGPS still a big challenge. Therefore, combining its optical properties, dot distribution, defect imaging characteristics and detection requirements, a surface defect detection algorithm based on LGP-YOLO for practical industrial applications is proposed in this paper. To enhance the feature extraction ability of the network without dimensionality reduction, expand the effective receptive field and reduce the interference of invalid targets, we built the receptive field module (RFM) by combining the effective channel attention network (ECA-Net) and reviewing large kernel design in CNNs (RepLKNet). For the purpose of optimizing the performance of the network in downstream tasks, enhance the network's expression ability and improve the network’s ability of detecting multi-scale targets, we construct the small detection module (SDM) by combining space-to-depth non-strided convolution (SPDConv) and omini-dimensional dynamic convolution (ODConv). Finally, an LGP defect dataset is constructed using a set of images collected from industrial sites, and a multi-round experiment is carried out to test the proposed method on the LGP detect dataset. The experimental results show that the proposed LGP-YOLO network can achieve high performance, with mAP and F1-score reaching 99.08% and 97.45% respectively, and inference speed reaching 81.15 FPS. This demonstrates that LGP-YOLO can strike a good balance between detection accuracy and inference speed, capable of meeting the requirements of high-precision and high-efficiency LGP defect detection in LGP manufacturing factories.

Birefringence-derived artifact in optical coherence tomography imaging of the lamina cribrosa in eyes with glaucoma

We investigated birefringence-derived artifacts that potentially mimic focal defects of the lamina cribrosa (focal LC defects) in optical coherence tomography (OCT) imaging of eyes with glaucoma. This study included 74 eyes of 48 patients with glaucoma. Five horizontal line B-scan images of the optic disc were obtained using commercial swept-source OCT. From a dataset of prototype swept-source polarization-diversity OCT, we calculated the following types of OCT images: polarization-dependent, polarization-dependent attenuation-coefficient, polarization-independent, and polarization-independent attenuation-coefficient. We assessed the commercial OCT images for the presence of birefringence-derived artifacts by comparison with the polarization-diversity OCT images. Commercial OCT showed suggestive findings of focal LC defects in 17 of 74 eyes. Reevaluation using polarization-independent OCT revealed that the focal LC defects in one of 17 eyes (5.9%) were actually birefringence-derived artifacts. This study demonstrated the existence of birefringence-derived artifacts mimicking focal LC defects in commercial OCT imaging and indicated that polarization-diversity OCT is an effective tool to evaluate the presence of these artifacts.