Defects In Region Research Articles

Evaluating the mechanism and the capacitive properties of nickel boride electrodes for supercapacitor applications

This study investigates the electrochemical properties of nickel boride electrodes produced via a novel molten salt boron diffusion method; known as cathodic reduction and thermal diffusion-based boriding (CRTD-Bor). Pure nickel substrates with two different geometries (i.e. flat and foam) were borided in the borax-based electrolyte. The presence of nickel borides, composed of NiB, Ni4B3 and Ni2B with the order locations from the surface towards the matrix, was confirmed by X-ray diffractometry (XRD) and scanning electron microscopy (SEM) attached with electron probe microanalyzer (EPMA). The electrochemical behaviors of the produced electrodes were inspected through cyclic voltammetry (CV), galvanostatic charge-discharge (GCD), as well as impedance spectroscopy (EIS). The areal capacitances of the electrodes were calculated as 478 and 1385 mF/cm2 for flat and foam samples, respectively after the 2000th cycle of CV at 10 mV/s scan rate. The reaction kinetic followed the mixed-controlled model according to the Trasatti approach. The capacitive contribution (Co) measured in flat samples was 71 %; whereas, it was found as 40.6 % in the foam-shaped electrodes. Additionally, the mechanism behind the faradaic reactions was explored through X-ray photoelectron spectroscopy (XPS) and Fourier Transform Infrared Spectroscopy (FTIR) which were conducted both before and after the CV experiments. The obtained results showed that the possible mechanism was related to the formation of NiO bonds due to the oxygen saturation in the defective regions of the surfaces during electrochemical testing. Moreover, the importance of the electrode geometry on the performance was determined by the fact that the porous structure leads to more energy storage and favors a fast charge-discharge trend.

Visual defect obfuscation based self-supervised anomaly detection

Due to scarcity of anomaly situations in the early manufacturing stage, an unsupervised anomaly detection (UAD) approach is widely adopted which only uses normal samples for training. This approach is based on the assumption that the trained UAD model will accurately reconstruct normal patterns but struggles with unseen anomalies. To enhance the UAD performance, reconstruction-by-inpainting based methods have recently been investigated, especially on the masking strategy of suspected defective regions. However, there are still issues to overcome: (1) time-consuming inference due to multiple masking, (2) output inconsistency by random masking, and (3) inaccurate reconstruction of normal patterns for large masked areas. Motivated by this, this study proposes a novel reconstruction-by-inpainting method, dubbed Excision And Recovery (EAR), that features single deterministic masking based on the ImageNet pre-trained DINO-ViT and visual obfuscation for hint-providing. Experimental results on the MVTec AD dataset show that deterministic masking by pre-trained attention effectively cuts out suspected defective regions and resolves the aforementioned issues 1 and 2. Also, hint-providing by mosaicing proves to enhance the performance than emptying those regions by binary masking, thereby overcomes issue 3. The proposed approach achieves a high performance without any change of the model structure. Promising results are shown through laboratory tests with public industrial datasets. To suggest EAR be possibly adopted in various industries as a practically deployable solution, future steps include evaluating its applicability in relevant manufacturing environments.

Enhanced recognition of insulator defects on power transmission lines via proposal-based detection model with integrated improvement methods

Deep learning-driven transmission line inspection is a critical area for smart power grid development. Despite advances in deep learning for insulator defect detection, challenges remain in model robustness and adaptability for the varying real-world adaptability, especially for insignificant defects in complex backgrounds. This study presents a comprehensive improvement strategy for detecting insulators and cross-scale broken defects on transmission lines, employing a proposal-based detection model. The model introduces a holistic pipeline of improved methods, including backbone modification, anchor box scale recalibration, and improvements in Region of Interest (RoI) downsampling alignment and Intersection over Union (IoU) loss function. Various backbone networks, including convolutional network (ConvNet) and Vision Transformer (ViT) structures, are constructed and integrated with attention modules, specifically designed to amplify the perception of insulators and defective regions. The geometric scale of anchor boxes is reconstructed using a developed clustering algorithm, considering the elongated characteristics of insulator strings to improve the adaptability of anchor boxes. Bilinear interpolation is utilized to mitigate spatial misalignment issues during the downsampling process of Region Proposal Network (RPN)-based proposals. The experimental results indicate that the improved models with the Swin Transformer (Swin-T) backbone framework achieve the mean Average Precision (mAP)@0.5 of 88.42% and mAP@0.7 of 60.52%, with a defect recall rate of 81.94%. Additionally, the improved IoU loss function contributes to the performance of the model at higher IoU thresholds. The results of this study contribute to the further development of defect detection frameworks for power vision applications.

Control interfacial charge transfer behavior by creating surface defects on structure unit of heterojunction to drive carrier separation for enhancing photocatalytic CO2 reduction

Controlling interfacial charge transfer behavior of heterojunction is an arduous issue to efficiently drive separation of photogenerated carriers for improving the photocatalytic activity. Herein, the interface charge transfer behavior is effectively controlled by fabricating an unparalleled VO-NiWO4/PCN heterojunction that is prepared by encapsulating NiWO4 nanoparticles rich in surface oxygen vacancies (VO-NiWO4) in the mesoporous polymeric carbon nitride (PCN) nanosheets. Experimental and theoretical investigations show that, differing with the traditional p-n junction, the direction of built-in electric field between p-type NiWO4 and n-type PCN is reversed interestingly. The strongly codirectional built-in electric field is also produced between the surface defect region and inside of VO-NiWO4 besides in the space charge region, the dual drive effect of which forcefully propels interface charge transfer through triggering Z-Scheme mechanism, thus significantly improving the separation efficiency of photogenerated carriers. Moreover, the unique mesoporous encapsulation structure of VO-NiWO4/PCN heterostructure can not only afford the confinement effect to improve the reaction kinetics and specificity in the CO2 reduction to CO, but also significantly reduce mass transfer resistance of molecular diffusion towards the reaction sites. Therefore, the VO-NiWO4/PCN heterostructure demonstrates the preeminent activity, stability and reusability for photocatalytic CO2 reduction to CO reaction. The average evolution rate of CO over the optimal 10 %-VO-NiWO4/PCN composite reaches around 2.5 and 1.8 times higher than that of individual PCN and VO-NiWO4, respectively. This work contributes a fresh design approach of interface structure in the heterojunction to control charge transfer behaviors and thus improve the photocatalytic performance.

Visual localization and quantitative detection method for thermal defects in cable terminals of high-speed trains based on a temperature derivative

Abstract Cable terminal defect detection plays an important role in ensuring the safe and stable operation of high-speed trains (HST). In this paper, a numerical model of the electromagnetic thermal field of overheating defects of cable terminal shielding grids and a method of detecting internal defects of cable terminals - even-order temperature derivative (EOTD) is proposed for quantitative detection of internal defective structures of vehicle-mounted cable terminals. Firstly, a numerical model of the electromagnetic thermal field of cable terminals under the condition of leakage current is constructed, through which the temperature field distribution characteristics of different defective structures are analyzed. Then, the intuitive location of the defective region is obtained by investigating the derivative characteristics of the temperature image, and the depth and intensity of the defects are quantitatively assessed by using the main side peak (MSP) distances and the MSP values extracted from the derivative curves. Finally, the simulation and experimental results achieve the identification of defect structures and the quantitative detection of defect depth and intensity, proving the effectiveness and accuracy of the proposed method.

Mathematical analysis of spin transport in topological insulators to optimize memory devices performance using numerical simulations

Understanding spin transport in topological insulators (TIs) is crucial for the development of spin-based technologies, such as magnetic memory, sensors, and quantum bits. To optimize memory device performance, we developed a comprehensive mathematical model that describes the evolution of spin density, as a function of space and time, taking into account spin-momentum locking, spin-orbit coupling, spin dynamics, and diffusive transport processes (including phonon and impurity scattering). Using numerical simulations (finite element method and Backward Euler Method) implemented in Python, we analyzed the spin transport in TIs. Our study demonstrated a critical dependence of spin transport efficiency on device length, with a maximum efficiency of 75% at a length of 8 micrometers. Beyond a critical length of 10 micrometers, efficiency recovered, reaching 80% at 14 micrometers. We observed oscillatory behavior in spin polarization, with amplitude modulation indicating constructive and destructive interference patterns. The inclusion of spin-orbit coupling and Dirac terms in our model revealed a non-uniform spin polarization distribution, with a 30% increase in polarization near the center. Defects and boundaries significantly impacted spin transport, reducing polarization by 40% near the defect region. Through optimization, we achieved a 25% increase in spin transport efficiency and a 20% enhancement in memory device performance. Our results demonstrate the crucial role of optimizing device length, material quality, and interface engineering in achieving efficient spin transport and improved memory device performance, paving the way for the development of high-performance spin-based technologies.

Electronic analogue of Fourier optics with massless Dirac fermions scattered by quantum dot lattice

Abstract The field of electron optics exploits the analogy between the movement of electrons or charged quasiparticles, primarily in two-dimensional materials subjected to electric and magnetic (EM) fields and the propagation of electromagnetic waves in a dielectric medium with varied refractive index. We significantly extend this analogy by introducing an electronic analogue of Fourier optics dubbed as Fourier electron optics (FEO) with massless Dirac fermions (MDF), namely the charge carriers of single-layer graphene under ambient conditions, by considering their scattering from a two-dimensional quantum dot lattice (TDQDL) treated within Lippmann–Schwinger formalism. By considering the scattering of MDF from TDQDL with a defect region, as well as the moiré pattern of twisted TDQDLs, we establish an electronic analogue of Babinet’s principle in optics. Exploiting the similarity of the resulting differential scattering cross-section with the Fraunhofer diffraction pattern, we construct a dictionary for such FEO. Subsequently, we evaluate the resistivity of such scattered MDF using the Boltzmann approach as a function of the angle made between the direction of propagation of these charge-carriers and the symmetry axis of the dot-lattice, and Fourier analyze them to show that the spatial frequency associated with the angle-resolved resistivity gets filtered according to the structural changes in the dot lattice, indicating wider applicability of FEO of MDF.

Detection of near-surface defects in viscoelastic material based on focused ultrasound thermal effects

Viscoelastic materials will absorb and dissipate energy under stress, resulting in energy loss and heat generation. The conventional non-destructive testing methods have certain limitations when it comes to detecting near-surface defects in viscoelastic materials. In this paper, a detection method of near-surface defects based on focused ultrasonic thermal effect is proposed. Firstly, the difference in thermal effects caused by defective and non-defective regions of the material under ultrasound is analyzed according to the stress response equation of viscoelastic materials, and the detection principle is elucidated. Secondly, the feasibility of this method is verified through finite element simulation with an example of plexiglass material Subsequently, the variations in the surface temperature distribution of defective specimens with varying diameters and depths are analyzed. Finally, experimental validation reveals that ultrasonic waves operating at 1.12 MHz successfully detect artificial defects with a diameter of 1 mm. With the increase of the equivalent diameter of the defect, the width of the low-temperature depression area in the surface temperature field exhibits a linear increase relationship. With the increase of the defect depth, the surface temperature difference between the corresponding position of the defective and the surrounding non-defective area gradually decreases. This method effectively overcomes the half-wavelength limitation and introduces a novel detection approach for near-surface defect identification in viscoelastic materials such as plexiglass.

Reinforced BiOBr for visible-light-driven nitrogen fixation: A synergistic effect of cocatalyst and oxygen vacancies

Photocatalytic nitrogen fixation is a promising long-term strategy for NH3 synthesis, with the development of effective and durable photocatalysts being crucial for achieving high-efficiency N2 to NH3 conversion. In this study, we employed vacancy and interface engineering to create bismuth (Bi) nanoparticles on hierarchical oxygen-deficient BiOBr microspheres using a one-step solvothermal approach. These Bi-decorated BiOBr microspheres, featuring oxygen vacancies, achieved a high photocatalytic ammonia synthesis rate of 222.3 μmol·g−1·h−1 in pure water under visible light irradiation, which is 3.25 times higher than that of the original BiOBr. Atomic-scale images of the interface between the defective regions of BiOBr and the Bi cocatalyst and several other material characterizations confirmed that the enhanced photoreduction activity of Bi/BiOBr is due to the synergistic effects of the metal Bi and the oxygen vacancies. These results present a straightforward and practical method for designing efficient photocatalysts for nitrogen fixation.

Design of a Facility for Simultaneous Irradiation with Two Ion Beams based on the HIPR Accelerator for Simulations Neutron Influence

Ion accelerator facility is a powerful tool to simulate neutron irradiation effects in reactor materials. Defects in the crystal lattice arise and the accumulation of transmutation products (helium and hydrogen) occurs in the structure of the material under the action of neutrons in the structural materials of nuclear installations. At Kurchatov Complex for Theoretical and Experimental Physics the heavy ion accelerator HIPr (heavy ion prototype) is used to simulate radiation damage in steels and alloys using a 5.6 MeV Fe2+ ion beam. The second beam line is designed at the HIPr facility to simultaneously implant helium (or hydrogen) into the region of defects. The second beam line provides a beam of helium ions with energy up to 300 keV. The report presents a description of second beam line design and a status of construction the second beam line.

Mathematical Modeling of the Heat Transfer Process in Spherical Objects with Flat, Cylindrical and Spherical Defects

This paper proposes a method for determining the optimal parameters for the thermal testing of plant tissues of fruits and vegetables containing surface and subsurface defects in the form of areas of plant tissues with different thermophysical characteristics. Based on well-known mathematical models for objects of predominantly flat, cylindrical and spherical shapes containing flat, spherical and cylindrical regions of defects, numerical solutions of three-dimensional, non-stationary temperature fields were found, making it possible to measure the power and time of the thermal exposure of the sample surface to the radiation from infrared lamps using the finite element method. This made it possible to ensure the reliable detection of a temperature contrast of up to 4 °C between the defect and defect-free regions of the test object using modern thermal imaging cameras. In this case, subsurface defects can be detected at a depth of up to 3 mm from the surface. To determine the parameters of mathematical models of temperature fields, such as thermal conductivity and a coefficient of the thermal diffusivity of plant tissues, a new method of a pulsed heat flux from a flat heater is proposed; this differs in the method of processing experimental data and makes it possible to determine the required characteristics with high accuracy during the active stage of the experiment in a period not exceeding 1–3 min.

ASD-YOLO: An aircraft surface defects detection method using deformable convolution and attention mechanism

Aircraft surface defect (ASD) detection is crucial for ensuring flight safety. Addressing challenges such as large-scale variations, irregular shapes, and sample imbalance in ASD detection, this paper proposes a ASD-YOLO network based on YOLOv5. ASD-YOLO incorporates several enhancements to improve recognition capabilities. Firstly, a new deformable convolutional feature extraction module (DCNC3) is designed to better learn defects of different shapes, which is combined with a global attention mechanism (GAM) to pay more attention to defects region information. Secondly, the feature representation of small defects is bolstered by the contextual enhancement module (CEM). Lastly, to alleviate sample imbalance problem, we introduce an exponential sliding average weight function (EMA-Slide). Experimental results on two datasets show improvements in mean Average Precision (mAP) by 5.7% and 3.4%, respectively, surpassing mainstream algorithms and offering a novel approach to ASD detection.

Modeling segregated solutes in plastically deformed alloys using coupled molecular dynamics-Monte Carlo simulations

A microscopic understanding of the complex solute-defect interaction is pivotal for optimizing the alloy’s macroscopic mechanical properties. Simulating solute segregation in a plastically deformed crystalline system at atomic resolution remains challenging. The objective is to efficiently model and predict a physically informed segregated solute distribution rather than simulating a series of diffusion kinetics. To address this objective, we coupled molecular dynamics (MD) and Monte Carlo (MC) methods using a novel method based on virtual atoms technique. We applied our MD-MC coupling approach to model off-lattice carbon (C) solute segregation in nanoindented Fe-C samples containing complex dislocation networks. Our coupling framework yielded the final configuration through efficient parallelization and localized energy computations, showing C Cottrell atmospheres near dislocations. Different initial C concentrations resulted in a consistent trend of C atoms migrating from less crystalline distortion to high crystalline distortion regions. Besides unraveling the strong spatial correlation between local C concentration and defect regions, our results revealed two crucial aspects of solute segregation preferences: (1) defect energetics hierarchy and (2) tensile strain fields near dislocations. The proposed approach is generic and can be applied to other material systems as well.

Attention Similarity Outlier Suppression with Hierarchical Feature Integration for Defect Segmentation

Defect segmentation holds significant importance in industrial applications. However, most existing algorithms are tailored to specific scenarios, lacking a unified defect segmentation algorithm for defect segmentation. Based on the analysis of the characteristics of defects, we aim to propose a unified transformer‐based defect segmentation algorithm. On the one hand, the defect foreground exhibits significant differences from the normal background. Nevertheless, the attention mechanism in the transformer involves all feature nodes in the feature map to the similarity score of one patch with all other patches. This approach may compromise the features of potential defect areas where the defect foreground significantly contrasts with the background. We introduce the Attention Similarity Outlier Suppression (ASOS) strategy to address this challenge. Our method is designed to select valid attention scores and filter out outlier scores for appropriate attention fusion, in which the feature of potential defect areas is not easily affected by background. On the other hand, the defect regions often have relatively small areas. We incorporate a Hierarchical Feature Integration (HFI) module in our framework to facilitate defect recognition at different scales. By progressively up‐sampling, features can achieve better integration to generate the final segmentation result. Extensive experiments have verified the effectiveness of our proposed method in defect segmentation tasks. Moreover, thorough ablation studies have demonstrated the efficacy of our algorithm, reinforcing its robust performance. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Analytical insight into local defect resonance using a two-step method

Local defect resonance (LDR) has gained increasing attention in the field of non-destructive testing due to its capacity to selectively amplify vibrations within the defect regions and to enable defect-selective and high-resolution imaging. How-ever, the conventional theory based on vibration method adopted for analyzing the fundamental frequencies of LDR deviates from experimental results when dealing with relatively thick defects. To tackle this deficiency, this study presents a two-step analytical methodology designed to precisely evaluate the local reso-nance effects of thick planar defects. In the first step, the normal-mode expansion method is employed to calculate the phase shift of the reflected elastic wave. In the second step, by using the standing wave's wavelength obtained in the first step, the Rayleigh method is taken into account to calculate LDR frequencies for various defect shapes. The refined theory is subsequently validated through finite element analysis and experimental measurement. The results clearly demonstrate that the LDR frequencies calculated using the refined theory exhibit a better agreement with experimental and simulated data.

Multi-perspective feature collaborative perception learning network for non-destructive detection of pavement defects

Pavement defects detection has made significant progress with the development of convolutional neural networks. Due to the topological complexity of pavement defects regions, most existing detection methods are limited to a limited number of types of defects, which results in unsatisfactory detection results for multi-classified defects. Several studies have used enhanced data to address this issue, however, few have considered the inefficiency caused by imbalanced differences between classes from the perspective of the network model itself. In light of this issue, our goal is to fill this gap by better exploring the fusion of different feature information to improve the performance of the network. As a result, we propose a multi-perspective feature collaborative perception learning network (MFCPLNet), which uses the model's contextual information to guide high-quality multi-classified defects detection and that can be adapted to various pavement conditions. Specifically, for adaptive feature extraction, an irregular perceptual extraction network (IPENet) with enhanced edge information is first designed to draw upon hybrid deformable convolution for efficient network block hybridization. Additionally, a multi-balanced collaborative learning mechanism (MCLM) consisting of auxiliary axial information and 3D feature guidance is constructed to enhance deep semantic features, improve the localization of defects in defective regions, and compensate for weak category supervision. Lastly, in the detection head, an iterative soft aggregation module has been designed to enhance scalable spatial interactions. The results of extensive testing were based on publicly available pavement defects datasets. Based on the proposed network, the mAP index reaches 29.6% and 33.8%, respectively, an increase of 8.3% and 4.6% over the baseline, which is qualitatively and quantitatively superior to mainstream methods.

Parameter automatic optimization strategy for laser powder bed fusion using neural network infrared radiation intensity prediction model

Conventional scanning approaches with fixed laser parameters are prone to causing localized defects and in-homogeneous performance when forming parts with complicated geometries using the laser powder bed fusion (LPBF) process. While online monitoring of the forming process using thermal cameras allows the identification of abnormal infrared radiation intensity values of the scanning line to pinpoint regions of potential defects, it fails to proactively prevent defects from occurring. To solve this problem, this study constructed a back propagation (BP) neural network to predict the infrared radiation intensity of the current layer before scanning. In the meantime, this study designed a parameter optimization algorithm to adjust the local scanning parameters with a view to avoiding defects and thus enhancing the performance consistency of the formed parts. This study drew on the idea of the divide-and-conquer method to divide the parts into multiple units for data acquisition by thermal camera, and has collected 500,000 pieces of experimental data through 15 experiments. By utilizing these data, this study trained a BP neural network to predict the infrared radiation intensity, and its mean absolute error (MAE) of the prediction results was 25, the coefficient of determination (R2) was 0.708, and the F1 score (abnormal radiation intensity identification and classification) was 0.802. Subsequently, the study optimized the scanning parameters of the unit by using particle swarm optimization (PSO), and performed practical application and validation on spherical gear parts of 316 L stainless steel and blade parts of DZ125 nickel-based superalloy. Findings indicated that the automatic optimization strategy can effectively prevent abnormal infrared radiation intensity values during the scanning process and enhance the performance consistency of the LPBF process for complicated parts in both different materials and structures.

Indirect magneto-ionic effect in FeSi2/Si nanocomposite induced by electrochemical lithiation and delithiation

A nanocomposite consisting of iron disilicide nanocrystals embedded in a Si matrix was prepared from industry-grade ferrosilicon by ball milling and subsequent heat treatment. By tailoring the heat treatment temperature either the metallic α-FeSi2 or the semiconducting β-FeSi2 phase could be made the dominant one, as indicated by x-ray diffraction. Magnetization curve and zero-field cooled/field cooled measurements revealed that ferromagnetic and superparamagnetic centers are present in the nanocomposites, which could be attributed to Fe-rich defective regions at the surface of the iron disilicide nanocrystals. For both nanocomposites, containing either mainly the α or β phase, we could show that the magnetization can be varied by about 40% by electrochemical lithiation and delithiation of the surrounding Si matrix, with up to 6.5% of the magnetization change being reversible. These variations could be attributed to the formation of additional Fe-rich magnetic regions, induced by a local change of the Fe/Si fraction at the FeSi2/Si interfaces, and their subsequent partial elimination. Thus, this work demonstrates a new concept for how an ‘indirect magneto-ionic effect’ can be obtained in composite materials consisting of a phase prone to the electrochemical ion uptake (i.e. the Si matrix) and a magnetic phase (i.e. the FeSi2 nanocrystals).

Research on the influence law of geomagnetic field on magnetic signals and compensation modeling

To study the influence law and mechanism of the geomagnetic field on magnetic signals in the process of magnetic memory testing, the linear scanning detection results of defective specimens with three different initial magnetization strengths (0 A/m, −70 A/m, 105 A/m) along the horizontal and vertical directions in the geomagnetic field ambient are analyzed and discussed. The result shows that the magnetic signals in the defect-free region only change in amplitude with the change of detection angle, and have no effect on the overall shape of the magnetic signal curve; the characteristic parameters Sm and Km of the peaks of the magnetic signals in the defective region show a corresponding relationship with the measurement angle in the horizontal detection. The initial magnetization strength of the specimen itself affects the change rule of the defective characteristic peaks. The mechanism analysis reveals that the extracted magnetic signals are affected by the combination of the geomagnetic field, the induced magnetic field of the specimen itself, and the leakage magnetic field. Finally, aiming at the problem that the geomagnetic field will change the topography of the defect peaks along different detection orientations, a geomagnetic compensation model is proposed for the barrel-shaped equipment when performing circumferential scanning detection, which can reduce the interference of the geomagnetic field aberration on the detection results, to prevent the misjudgment and omission of the specimen’s defect location.

RADDA-Net: Residual attention-based dual discriminator adversarial network for surface defect detection

Surface defect detection is a critical yet challenging task in the product production process, mainly because of the complex background texture on the material surface, the large intra-class defect differences, and the weak texture defect regions. To address this problem, a Residual Attention-based Dual Discriminator Adversarial Network (RADDA-Net) is developed to accurately detect various types of texture defect regions. Specifically, a residual network-based feature extraction module integrating multi-channel attention mechanism and multi-scale convolutional layers is designed to improve the detection ability of abnormal defects. Besides, to deeply excavate material surface texture information, the pixel attention-based up-sampling blocks are also incorporated into RADDA-Net to enhance the extraction of shallow features. To further improve the detection performance of RADDA-Net, a dual discriminant adversarial training strategy and a comprehensive measurement method are introduced to assist the developed network to identify defects from both macroscopic structure and microscopic edges. Finally, the six evaluation indexes of our proposed method outperformed other state-of-the-art methods on the four defect datasets, among which the highest detection precision, recall, and F-measure are 0.926, 0.916, 0.920, respectively. Therefore, the experimental results consistently verify the detection accuracy and effectiveness of RADDA-Net.