Defect Contrast Research Articles

A Strip Steel Surface Defect Salient Object Detection Based on Channel, Spatial and Self-Attention Mechanisms

Strip steel is extensively utilized in industries such as automotive manufacturing and aerospace due to its superior machinability, economic benefits, and adaptability. However, defects on the surface of steel strips, such as inclusions, patches, and scratches, significantly affect the performance and service life of the product. Therefore, the salient object detection of surface defects on strip steel is crucial to ensure the quality of the final product. Many factors, such as the low contrast of surface defects on strip steel, the diversity of defect types, complex texture structures, and irregular defect distribution, hinder existing detection technologies from accurately identifying and segmenting defect areas against complex backgrounds. To address the above problems, we propose a novel detector called S3D-SOD for the salient object detection of strip steel surface defects. For the encoding stage, a residual self-attention block is proposed to explore semantic information cues of high-level features to locate and guide low-level feature information. In addition, we apply a general residual channel and spatial attention to low-level features, enabling the model to adaptively focus on the key channels and spatial areas of feature maps with high resolutions, thereby enhancing the encoder features and accelerating the convergence of the model. For the decoding stage, a simple residual decoder block with an upsampling operation is proposed to realize the integration and interaction of feature information between different layers. Here, the simple residual decoder block is used for feature integration due to the following observation: backbone networks like ResNet and the Swin Transformer, after being pretrained on the large dataset ImageNet and then fine-tuned on a smaller dataset for strip steel surface defects, are capable of extracting feature maps that contain both general image features and the specific characteristics required for the salient object detection of strip steel surface defects. The experimental results on the SD-saliency-900 dataset show that S3D-SOD is better than advanced methods, and it has strong generalization ability and robustness.

Anisotropy of radiation-induced defects in Yb-implanted β-Ga2O3

RE-doped β-Ga2O3 seems attractive for future high-power LEDs operating in high irradiation environments. In this work, we pay special attention to the issue of radiation-induced defect anisotropy in β-Ga2O3, which is crucial for device manufacturing. Using the RBS/c technique, we have carefully studied the structural changes caused by implantation and post-implantation annealing in two of the most commonly used crystallographic orientations of β-Ga2O3, namely the (-201) and (010). The analysis was supported by advanced computer simulations using the McChasy code. Our studies reveal a strong dependence of the structural damage induced by Yb-ion implantation on the crystal orientation, with a significantly higher level of extended defects observed in the (-201) direction than for the (010). In contrast, the concentration and behavior of simple defects seem similar for both oriented crystals, although their evolution suggests the co-existence of two different types of defects in the implanted zone with their different sensitivity to both, radiation and annealing. It has also been found that Yb ions mostly occupy the interstitial positions in β-Ga2O3 crystals that remain unchanged after annealing. The location is independent of the crystal orientations. We believe that these studies noticeably extend the knowledge of the radiation-induced defect structure, because they dispel doubts about the differences in the damage level depending on crystal orientation, and are important for further practical applications.

Recognizing Microwave Field Contrast of Invisible Microstrip Defects With High Accuracy by Quantum Wide-Field Microscope

Recognizing Microwave Field Contrast of Invisible Microstrip Defects With High Accuracy by Quantum Wide-Field Microscope

Illumination Correction and Enhancement Algorithm for Defective Powder Spreading Images in Selective Laser Sintering

Abstract Based on the uneven illumination distribution and low defect contrast in the powder spreading images acquired during the selective laser sintering manufacturing process, which affects subsequent defect detection, an adaptive nonlinear illumination correction and enhancement algorithm is proposed. Local illumination correction is performed using gray valueitting curves and is combined with global gamma correction to improve correction capability in overly dark areas. A nonlinear enhancement function is constructed to boost the contrast of the image’s illumination component. Finally, a gradient-based L0 norm minimization algorithm is used to smooth non-defect areas. Experimental results show that the proposed algorithm signi icantly improves correction and enhancement, outperforming comparison algorithms overall. Compared to the original images, the coef icient of variation decreases by an average of 0.179, and the standard deviation increases by an average of 26.7%, demonstrating the effectiveness of the proposed algorithm.

FDADNet: Detection of Surface Defects in Wood-Based Panels Based on Frequency Domain Transformation and Adaptive Dynamic Downsampling

The detection of surface defects on wood-based panels plays a crucial role in product quality control. However, due to the complex background and low contrast of defects in wood-based panel images, features extracted by traditional deep learning methods based on spatial domain processing often contain noise and blurred boundaries, which severely affects detection performance. To address these issues, we have proposed a wood-based panel surface defect detection method based on frequency domain transformation and adaptive dynamic downsampling (FDADNet). Specifically, we designed a Multi-axis Frequency Domain Weighted Information Representation Module (MFDW), which effectively decoupled the indistinguishable low-contrast defects from the background in the transform domain. Gaussian filtering was then employed to eliminate noise and blur between the defects and the background. Additionally, to tackle the issue of scale differences in defects that led to difficulties in accurate capture, we designed an Adaptive Dynamic Convolution (ADConv) module for downsampling. This method flexibly compressed and enhanced features, effectively improving the differentiation of the features of objects of varying scales in the transform space, and ultimately achieved effective defect detection. To compensate for the lack of data, we constructed a dataset of wood-based panel surface defects, WBP-DET. The experimental results showed that the proposed FDADNet effectively improved the detection performance of wood-based panel surface defects in complex scenarios, achieving a solid balance between efficiency and accuracy.

Image Quality of Cardiac Silicon Photomultiplier PET/CT Using an Infant Phantom of Extremely Low Birth Weight.

The lack of pediatrics-specific equipment for nuclear medicine imaging has resulted in insufficient diagnostic information for newborns, especially low-birth-weight infants. Although PET offers high spatial resolution and low radiation exposure, its use in newborns is limited. This study investigated the feasibility of cardiac PET imaging using the latest silicon photomultiplier (SiPM) PET technology in infants of extremely low birth weight (ELBW) using a phantom model. Methods: The study used a phantom model representing a 500-g ELBW infant with brain, cardiac, liver, and lung tissues. The cardiac tissue included a 3-mm-thick defect mimicking myocardial infarction. Organ tracer concentrations were calculated assuming 18F-FDG myocardial viability scans and 18F-flurpiridaz myocardial perfusion scans and were added to the phantom organs. Imaging was performed using an SiPM PET/CT scanner with a 5-min acquisition. The data acquired in list mode were reconstructed using 3-dimensional ordered-subsets expectation maximization with varying iterations. Image evaluation was based on the depiction of the myocardial defect compared with normal myocardial accumulation. Results: Increasing the number of iterations improved the contrast of the myocardial defect for both tracers, with 18F-flurpiridaz showing higher contrast than 18F-FDG. However, even at 50 iterations, both tracers overestimated the defect accumulation. A bull's-eye image can display the flow metabolism mismatch using images from both tracers. Conclusion: SiPM PET enabled cardiac PET imaging in a 500-g ELBW phantom with a 1-g heart. However, there were limitations in adequately depicting these defects. Considering the image quality and defect contrast,18F-flurpiridaz appears more desirable than 18F-FDG if only one of the two can be used.

Thermal Battery Multi-Defects Detection and Discharge Performance Analysis Based on Computed Tomography Imaging

To address the typical structural defects that are prone to occur during the preparation and storage processes of thermal battery, experiments of battery image acquisition were designed based on X-ray computed tomography system. An improved Yolov5s network was employed to achieve high-precision automatic detection of typical defects. Through the discharge experiment of thermal battery, discharge performance curves of normal batteries and three defective batteries were constructed. The impact and mechanisms of different defects on the discharge performance were analyzed based on the voltage curve. By designing an automatic stitching scheme, the phenomenon of interlayer information overlap caused by the increase of cone angle in digital radiography images was suppressed. To address the issues of low image contrast and limited defect data in thermal battery imaging, the defect dataset was expanded using the designed image preprocessing steps and improving the contrast of the images. For subtle defects that are difficult to identify, the introduced multi-head self-attention mechanism in Transformer and the use of Focal Loss instead of cross-entropy loss function were employed to improve the recognition accuracy of subtle defects while ensuring the detection speed. The comparative experiment shows that the improved network model has higher recognition accuracy compared to Faster R-CNN, SSD, Cascade R-CNN, EfficientDet and the original Yolov5s network. The recognition accuracy of typical defects in thermal batteries can reach 98.7%.

An optimized filter design approach for enhancing imaging quality in industrial linear accelerator.

The polychromatic X-rays generated by a linear accelerator (Linac) often result in noticeable hardening artifacts in images, posing a significant challenge to accurate defect identification. To address this issue, a simple yet effective approach is to introduce filters at the radiation source outlet. However, current methods are often empirical, lacking scientifically sound metrics. This study introduces an innovative filter design method that optimizes filter performance by balancing the impact of ray intensity and energy on image quality. Firstly, different spectra under various materials and thicknesses of filters were obtained using GEometry ANd Tracking (Geant4) simulation. Subsequently, these spectra and their corresponding incident photon counts were used as input sources to generate different reconstructed images. By comprehensively comparing the intensity differences and noise in images of defective and non-defective regions, along with considering hardening indicators, the optimal filter was determined. The optimized filter was applied to a Linac-based X-ray computed tomography (CT) detection system designed for identifying defects in graphite materials within high-temperature gas-cooled reactor (HTR), with defect dimensions of 2 mm. After adding the filter, the hardening effect reduced by 22%, and the Defect Contrast Index (DCI) reached 3.226. The filter designed based on the parameters of Average Difference (AD) and Defect Contrast Index (DCI) can effectively improve the quality of defect images.

Context-Enhanced Network with Spatial-Aware Graph for Smartphone Screen Defect Detection.

Interactive devices such as touch screens have gained widespread usage in daily life; this has directed the attention of researchers to the quality of screen glass. Consequently, defect detection in screen glass is essential for improving the quality of smartphone screens. In recent years, defect detection methods based on deep learning have played a crucial role in improving detection accuracy and robustness. However, challenges have arisen in achieving high-performance detection due to the small size, irregular shapes and low contrast of defects. To address these challenges, this paper proposes CE-SGNet, a Context-Enhanced Network with a Spatial-aware Graph, for smartphone screen defect detection. It consists of two novel components: the Adaptive Receptive Field Attention Module (ARFAM) and the Spatial-aware Graph Reasoning Module (SGRM). The ARFAM enhances defect features by adaptively extracting contextual information to capture the most relevant contextual region of defect features. The SGRM constructs a region-to-region graph and encodes region features with spatial relationships. The connections among defect regions are enhanced during the propagation process through a graph attention network. By enriching the feature representations of defect regions, the CE-SGNet can accurately identify and locate defects of various shapes and scales. Experimental results demonstrate that the CE-SGNet achieves outstanding performance on two public datasets.

Non-invasive inspection for a hand-bound book of the 19th century: Numerical simulations and experimental analysis of infrared, terahertz, and ultrasonic methods

Due to fungal growth and mishandling in the book, there are various types of defects as they age such as foxing, tears, and creases. It is important to develop novel non-invasive inspection techniques and defect recognition algorithms. In this work, three non-invasive inspection techniques, including infrared thermography (IRT), terahertz time-domain spectroscopy (THz-TDS), and air-coupled ultrasound (ACU), were employed for the detection of defects in an ancient book cover. To improve the image quality and defect contrast, principal component analysis, fast Fourier transform, and partial least squares regression algorithms are used as the post-processing methods. Furthermore, the YOLOv7 network is deployed for defect automatic detection. Finite element analysis and finite-difference time-domain methods were employed for generating training dataset of YOLOv7 network. Experimental results demonstrate that IRT and THz-TDS has excellent detection capability for surface and subsurface defects, respectively. By employing YOLOv7 network with simulation datasets, defects can be effectively identified.

Detection method of organic light-emitting diodes based on small sample deep learning.

In order to solve the surface detection problems of low accuracy, low precision and inability to automate in the production process of late-model display panels, a little sample-based deep learning organic light-emitting diodes detection model SmartMuraDetection is proposed. First, aiming at the detection difficulty of low surface defect contrast, a gradient boundary enhancement algorithm module is designed to automatically identify and enhance defects and background gray difference. Then, the problem of insufficient little sample data sets is solved, Poisson fusion image enhancement module is designed for sample enhancement. Then, a TinyDetection model adapted to small-scale target detection is constructed to improve the detection accuracy of defects in small-scale targets. Finally, SEMUMaxMin quantization module is proposed as a post-processing module for the result images derived from network model reasoning, and accurate defect data is obtained by setting a threshold filter. The number of sample images in the experiment is 334. This study utilizes an organic light-emitting diodes detection model. The detection accuracy of surface defects can be improved by 85% compared with the traditional algorithm. The method in this paper is used for mass production evaluation in the actual display panel production site. The detection accuracy of surface defects reaches 96%, which can meet the mass production level of the detection equipment in this process section.

Double-slit asymmetrical dynamical diffraction of X-rays in ideal crystals.

The theoretical investigation of double-slit asymmetrical dynamical diffraction of X-rays in perfect crystals establishes that Young's interference fringes on the exit surface are formed. The position of the fringes in the cross section of the beam depends on deviation from the Bragg exact orientation and asymmetry angle. An equation for the period of the fringes is presented, according to which the period is polarization sensitive. The period increases with increasing the absolute value of the asymmetry angle. In its turn, the size of the interference region also increases with increasing the absolute value of the asymmetry angle. However, the ratio of interference region size to period, i.e. the number of observed fringes, decreases with increasing the absolute value of the asymmetry angle. The size of the interference region can be of the order of a few tens of mm, which can be used for obtaining Fourier dynamical diffraction holograms of a large size. This type of diffraction can also be used for obtaining double-slit dynamical diffraction contrast of defects and deformations. Due to the phase difference information, in comparison with single-slit diffraction, double-slit diffraction is more sensitive to the existence of objects and deformations in the path of the wave.

Research on defect detection of bottle cap interior based on low-angle and large divergence angle vision system.

During the machine vision inspection of the inner section of bottle caps within pharmaceutical packaging, the unique conca bottom and convex side walls often create obstructions to the illumination. Consequently, this results in challenges such as irregular background and diminished feature contrast in the image, ultimately leading to the misidentification of defects. As a solution, a vision system characterized by a Low-Angle and Large Divergence Angle (LALDA) is presented in this paper. Using the large divergence angle of LED, combined with low-angle illumination, a uniform image of the side wall region with bright-field characteristics and a uniform image of inner circle region at the bottom with dark-field characteristics are obtained, thus solving the problems of light being obscured and brightness overexposure of the background. Based on the imaging characteristics of LALDA, a multi-channel segmentation (MCS) algorithm is designed. The HSV color space has been transformed, and the image is automatically segmented into multiple sub-regions by mutual calculation of different channels. Further, image homogenization and enhancement are used to eliminate fluctuations in the background and to enhance the contrast of defects. In addition, a variety of defect extraction methods are designed based on the imaging characteristics of different sub-regions, which can avoid the problem of over-segmentation in detection. In this paper, the LALDA is applied to the defect detection inside the cap of capsule medicine bottle, the detection speed is better than 400 pcs/min and the detection accuracy is better than 95%, which can meet the actual production line capacity and detection requirements.

Application of microwave holographic subsurface technology for non-destructive testing of reinforced polyurethane foam

Glass fiber Reinforced Polyurethane Foam (RPUF) has found wide application primarily in the aerospace and construction industry due to its outstanding properties. Unlike pure non-reinforced Polyurethane Foam (PUF), it has increased strength characteristics and at the same time retains unique heat and sound insulation properties. The paper considers the features of reinforced polyurethane foam examination in the Microwave (MW) range. It is shown that there is a significant difference in the recorded MW images compared to pure polyurethane foam. This is expressed primarily in the fact that the reinforcing fibers have a dielectric constant that is different from enclosing polyurethane foam, which leads to scattering and reflection of the incident electromagnetic wave on them. Experimental studies have shown that for RPUF, in contrast to PUF, there is an optimal wavelength range in which the contrast of defects against the background of glass fiber reflections is of the greatest value. An experimental comparison of two methods of examination back-scattering and forward-scattering methods was also carried out for RPUF. It is shown that the forward-scattering technology of measurements, if it can be implemented, has certain advantages since allows reducing the contrast of background reflections from the reinforcing fibers.

Application of microwave holographic subsurface technology for non-destructive testing of reinforced polyurethane foam

Glass fiber Reinforced Polyurethane Foam (RPUF) has found wide application primarily in the aerospace and construction industry due to its outstanding properties. Unlike pure non-reinforced Polyurethane Foam (PUF), it has increased strength characteristics and at the same time retains unique heat and sound insulation properties. The paper considers the features of reinforced polyurethane foam examination in the Microwave (MW) range. It is shown that there is a significant difference in the recorded MW images compared to pure polyurethane foam. This is expressed primarily in the fact that the reinforcing fibers have a dielectric constant that is different from enclosing polyurethane foam, which leads to scattering and reflection of the incident electromagnetic wave on them. Experimental studies have shown that for RPUF, in contrast to PUF, there is an optimal wavelength range in which the contrast of defects against the background of glass fiber reflections is of the greatest value. An experimental comparison of two methods of examination back-scattering and forward-scattering methods was also carried out for RPUF. It is shown that the forward-scattering technology of measurements, if it can be implemented, has certain advantages since allows reducing the contrast of background reflections from the reinforcing fibers.

Improved STMask R-CNN-based defect detection model for automatic visual inspection of an optics lens.

A lens defect is a common quality issue that has seriously harmed the scattering characteristics and performance of optical elements, reducing the quality consistency of the finished products. Furthermore, the energy hotspots coming from the high-energy laser through diffraction of optical component defects are amplified step by step in multi-level laser conduction, causing serious damage to the optical system. Traditional manual detection mainly relies on experienced workers under a special light source environment with high labor intensity, low efficiency, and accuracy. The common machine vision techniques are incapable of detecting low contrast and complex morphological defects. To address these challenges, a deep learning-based method, named STMask R-CNN, is proposed to detect defects on the surface and inside of a lens in complex environments. A Swin Transformer, which focuses on improving the modeling and representation capability of the features in order to improve the detection performance, is incorporated into the Mask R-CNN in this case. A challenge dataset containing more than 3800 images (18000 defect sample targets) with five different types of optical lens defects was created to verify the proposed approach. According to our experiments, the presented STMask R-CNN reached a precision value of 98.2%, recall value of 97.7%, F1 score of 97.9%, mAP@0.5 value of 98.1%, and FPS value of 24 f/s, which outperformed the SSD, Faster R-CNN, and YOLOv5. The experimental results demonstrated that the proposed STMask R-CNN outperformed other popular methods for multiscale targets, low contrast target detection and nesting, stacking, and intersecting defects sample detection, exhibiting good generalizability and robustness, as well as detection speed to meet mechanical equipment production efficiency requirements. In general, this research offers a favorable deep learning-based method for real-time automatic detection of optical lens defects.

Online micro defects detection for ductile cast iron pipes based on twin light photometric stereo

Ductile cast iron pipes (DCIPs) are widely utilized in water and gas supply due to their exceptional mechanical and material properties. Quality control during production is paramount for optimal performance, with surface defect detection being of utmost importance. Currently, production sites primarily rely on manual visual inspection, which presents several challenges, including high labor intensity, low efficiency, and subjectivity in defect judgment. 2D detection algorithms based on single images struggle to effectively detect small defects such as pores, pinholes, and scratches, resulting in high missed detection rates and vulnerability to interference from oil and water stains. To address these issues, this paper proposes a symmetrical twin-light photometric stereo (TLPS) detection algorithm based on photometric stereo theory, and image correction algorithm for mitigating the effects of ambient light. A visual inspection prototype system was designed and successfully implemented at a production site. The system comprises eight visual inspection units, each equipped with a high-speed line scan camera and two bar light sources for image acquisition of moving DCIP. Under the Lambertian model and parallel light source assumption, the gradient expression in the texture direction of the DCIP surface is derived based on light source symmetry. Production experiment results demonstrate that the gradient map obtained through the proposed TLPS effectively enhances defect contrast and accurately detects and locates micro defects such as pores, pinholes, and scratches on cast pipe surfaces. This verifies the effectiveness of the TLPS algorithm, addresses the shortcomings of traditional 2D detection algorithms, and lays the foundation for subsequent online detection of DCIP surface defects.

Myocardial perfusion imaging with retrospective gating and integrated correction of attenuation, scatter, respiration, motion, and arrhythmia

BackgroundAbsolute quantitative myocardial perfusion SPECT requires addressing of aleatory and epistemic uncertainties in conjunction with providing image quality sufficient for lesion detection and characterization. Iterative reconstruction methods enable the mitigation of the root causes of image degradation. This study aimed to determine the feasibility of a new SPECT/CT method with integrated corrections attempting to enable absolute quantitative cardiac imaging (xSPECT Cardiac; xSC).MethodsWe compared images of prototype xSC and conventional SPECT (Flash3DTM) acquired at rest from 56 patients aged 71 ± 12 y with suspected coronary heart disease. The xSC prototype comprised list-mode acquisitions with continuous rotation and subsequent iterative reconstructions with retrospective electrocardiography (ECG) gating. Besides accurate image formation modeling, patient-specific CT-based attenuation and energy window-based scatter correction, additionally we applied mitigation for patient and organ motion between views (inter-view), and within views (intra-view) for both the gated and ungated reconstruction. We then assessed image quality, semiquantitative regional values, and left ventricular function in the images.ResultsThe quality of all xSC images was acceptable for clinical purposes. A polar map showed more uniform distribution for xSC compared with Flash3D, while lower apical count and higher defect contrast of myocardial infarction (p = 0.0004) were observed on xSC images. Wall motion, 16-gate volume curve, and ejection fraction were at least acceptable, with indication of improvements. The clinical prospectively gated method rejected beats ≥20% in 6 patients, whereas retrospective gating used an average of 98% beats, excluding 2% of beats. We used the list-mode data to create a product equivalent prospectively gated dataset. The dataset showed that the xSC method generated 18% higher count data and images with less noise, with comparable functional variables of volume and LVEF (p = ns).ConclusionsQuantitative myocardial perfusion imaging with the list-mode-based prototype xSPECT Cardiac is feasible, resulting in images of at least acceptable image quality.

Comparison in the transient regime of four lock-in thermography algorithms by means of synthetic and experimental data on CFRP

Lock-in thermography is considered a well-established technique for investigating fiber-reinforced polymer composite (FRP) materials. These materials are characterized by a low thermal diffusivity and for this motivation, lock-in tests on composite materials generally are time consuming and then, for some applications, not suitable for industrial in-situ inspections. A way for reducing the testing time is to carry out the lock-in tests in the transient regime, although the most well-known lock-in algorithms were originally developed for the post-processing analysis in the steady-state regime. In this regard, the aim of this paper is to present a comprehensive comparison, in the transient regime, of four well-known lock-in thermography algorithms: the Signal Reconstruction (SR), Fast Fourier Transform (FFT), Dual Integration (DI), and Four Points method (4PM). In particular, to evaluate and compare how the transient regime can affect the accuracy and precision of the algorithms in determining the phase contrast of defects, several experimental tests were carried out on a carbon fibre-reinforced polymer (CFRP) test piece having different artificial defects by modulating the sine excitation periods by means of halogen lamps. Furthermore, synthetic data were used for simulating and investigating the four lock-in algorithms in a steady-state regime, as well. In addition, in order to evaluate the robustness of each algorithm, some offline processing analyses were carried out on the experimental data to evaluate the performance of each algorithm as a function of two user-controllable parameters, the number of frames and the number of cycles, and one not directly controlled by the operators, the dropped frame. For each analysis, qualitative and quantitative results in terms of phase and normalized phase contrast are presented and discussed.

Intelligent Detection Algorithm Based on 2D/3D-UNet for Internal Defects of Carbon Fiber Composites

ABSTRACT Infrared thermography testing (IRT) has been widely used in the defect detection of composite materials. However, the identification of defects characteristics is unsatisfying due to the interference of factors such as uneven background and noise in the original thermal image sequence. A novel thermography-based defect detection method with the semantic segmentation network is proposed to enhance the defect contrast and extract perfect features. To Figure out the abnormal distribution of temperature field in thermal images, AG-UNet was used with a spatial self-attention gate module to extract spatial features of thermal images. The 3D-UNet network was obtained by the 3D convolution module and the temporal convolution module to extract thermal temporal and spatial features simultaneously which could help the defect segmentation in thermal videos. Compared with traditional algorithms such as principal component analysis (PCA), thermographic signal reconstruction (TSR), and fast Fourier transform (FFT), defects detection results were significantly enhanced with the proposed method, and defects of smaller diameter-to-depth ratio can be detected by deep learning models.