High Detection Efficiency Research Articles

A P+ pocket doped 4H–SiC Schottky barrier FET as highly sensitive label-free biosensor

In this paper, a 4H–SiC Schottky barrier field-effect transistor with a P+ doping Pocket based on dielectric modulation effect for label-free detection of biomolecules has been proposed. Upon optimization of the length and position of P+ doping pocket, the sensitivity of the biosensor achieves the maximum when it is located within the channel 1 nm away from the Schottky contact with a length of 5 nm. Simulations have been performed for different dielectric constants and biomolecules with different charge densities by Sentaurus TCAD. The results show that the new structure has an on-current sensitivity of 1.03 × 108, a maximum transconductance sensitivity of 6.24 × 107, a threshold voltage sensitivity of 65 mV, and an Ion/Ioff ratio sensitivity of 1.8 × 104 for neutral biomolecules with K = 12. In addition, the fill factor of the cavity and linearity of the proposed structure are considered in this paper. Further, the on-current sensitivity of our proposed structure is gauged against state of the art, which demonstrate a high sensitivity of our proposed biosensors. The smaller size and prominent sensitivity characteristics of the biosensor proposed in this paper have made it widely used in the field of biomedical detection that requires high integration and efficient detection.

Triple-enhanced Raman scattering sensor based on superhydrophobic/-philic microsphere platform and heating evaporation assisted for high-sensitivity detection

The ability to detect molecules in small volumes of highly diluted solutions is crucial for applications such as food quality and safety, environmental monitoring, biomedical diagnostics, and so on. The primary concern lies in the sensitivity and efficiency of detection technology. In this work, an ultra-sensitive surface-enhanced Raman scattering (SERS) detection strategy is presented, which is based on superhydrophobic/-philic microsphere SERS (SSM-SERS) platform and heating evaporation assisted for high-sensitivity detection. Relying on the heating-assisted evaporation, the evaporation efficiency of the droplet is increased by 378.5 times, and the deposition area after evaporation is reduced by 10.4 times, leading to the extremely high detection efficiency and further enrichment of the target molecules. When the microspheres array approaches the superhydrophobic/-philic (SH/SHL) substrate, the optical-plasmonic hybrid structure is formed, enhancing light-matter interactions and resulting in an improvement of the Raman signal. The optimized SERS sensor is capable of detecting Rhodamine 6 G (R6G) at the femtomolar level (10−16 M), with a sample volume of only 5 μL and an enhancement factor of 3.29 × 109. Considering the misuse of fish drugs, malachite green (MG) and methylene blue (MB) were chosen to validate the performance of SERS sensor. These findings highlight the consistent high sensitivity of the SERS sensor, offering valuable insights for applications in food safety and related fields.

Highly sensitive volumetric single-molecule imaging.

Volumetric subcellular imaging has long been essential for studying structures and dynamics in cells and tissues. However, due to limited imaging speed and depth of field, it has been challenging to perform live-cell imaging and single-particle tracking. Here we report a 2.5D fluorescence microscopy combined with highly inclined illumination beams, which significantly reduce not only the image acquisition time but also the out-of-focus background by ∼2-fold compared to epi-illumination. Instead of sequential z-scanning, our method projects a certain depth of volumetric information onto a 2D plane in a single shot using multi-layered glass for incoherent wavefront splitting, enabling high photon detection efficiency. We apply our method to multi-color immunofluorescence imaging and volumetric super-resolution imaging, covering ∼3-4 µm thickness of samples without z-scanning. Additionally, we demonstrate that our approach can substantially extend the observation time of single-particle tracking in living cells.

Detection of Mulberry Leaf Diseases in Natural Environments Based on Improved YOLOv8

Mulberry leaves, when infected by pathogens, can suffer significant yield loss or even death if early disease detection and timely spraying are not performed. To enhance the detection performance of mulberry leaf diseases in natural environments and to precisely locate early small lesions, we propose a high-precision, high-efficiency disease detection algorithm named YOLOv8-RFMD. Based on improvements to You Only Look Once version 8 (YOLOv8), we first proposed the Multi-Dimension Feature Attention (MDFA) module, which integrates important features at the pixel-level, spatial, and channel dimensions. Building on this, we designed the RFMD Module, which consists of the Conv-BatchNomalization-SiLU (CBS) module, Receptive-Field Coordinated Attention (RFCA) Conv, and MDFA, replacing the Bottleneck in the model’s Residual block. We then employed the ADown down-sampling structure to reduce the model size and computational complexity. Finally, to improve the detection precision of small lesion features, we replaced the Complete Intersection over Union (CIOU) loss function with the Normalized Wasserstein Distance (NWD) loss function. Results show that the YOLOv8-RFMD model achieved a mAP50 of 94.3% and a mAP50:95 of 67.8% on experimental data, representing increases of 2.9% and 4.3%, respectively, compared to the original model. The model size was reduced by 0.53 MB to just 5.45 MB, and the GFLOPs were reduced by 0.3 to only 7.8. YOLOv8-RFMD has displayed great potential for application in real-world mulberry leaf disease detection systems and automatic spraying operations.

Development of an Imaging Spectrometer with a High Signal-to-Noise Ratio Based on High Energy Transmission Efficiency for Soil Organic Matter Detection.

Hyperspectral detection of the change rate of organic matter content in agricultural remote sensing requires a high signal-to-noise ratio (SNR). However, due to the large number and efficiency limitation of the components, it is difficult to improve the SNR. This study uses high-efficiency convex grating with a diffraction efficiency exceeding 50% across the 360-850 nm range, a back-illuminated Complementary Metal Oxide Semiconductor (CMOS) detector with a 95% efficiency in peak wavelength, and silver-coated mirrors to develop an imaging spectrometer for detecting soil organic matter (SOM). The designed system meets the spectral resolution of 10 nm in the 360-850 nm range and achieves a swath of 100 km and a spatial resolution of 100 m at an orbital height of 648.2 km. This study also uses the basic structure of Offner with fewer components in the design and sets the mirrors of the Offner structure to have the same sphere, which can achieve the rapid adjustment of the co-standard. This study performs a theoretical analysis of the developed Offner imaging spectrometer based on the classical Rowland circular structure, with a 21.8 mm slit length; simulates its capacity for suppressing the +2nd-order diffraction stray light with the filter; and analyzes the imaging quality after meeting the tolerance requirements, which is combined with the surface shape characteristics of the high-efficiency grating. After this test, the grating has a diffraction efficiency above 50%, and the silver-coated mirrors have a reflection value above 95% on average. Finally, the laboratory tests show that the SNR over the waveband exceeds 300 and reaches 800 at 550 nm, which is higher than some current instruments in orbit for soil observation. The proposed imaging spectrometer has a spectral resolution of 10 nm, and its modulation transfer function (MTF) is greater than 0.23 at the Nyquist frequency, making it suitable for remote sensing observation of SOM change rate. The manufacture of such a high-efficiency broadband grating and the development of the proposed instrument with high energy transmission efficiency can provide a feasible technical solution for observing faint targets with a high SNR.

Performance and detection range of acoustic receivers in mangrove habitats.

Acoustic telemetry has been used to monitor the movement of aquatic animals in a broad range of aquatic environments. Despite their importance, mangrove habitats are understudied for the spatial ecology of elasmobranchs, with acoustic telemetry rarely used inside mangrove habitats. One reason for this may be a general assumption that acoustic signals would not be able to be detected by receivers in such shallow, structurally complex, environments. This study tested whether acoustic receivers can be used inside mangrove habitats to track the movement of sharks and rays. Thirty-eight receivers were deployed in a mangrove system in Pioneer Bay, Orpheus Island, Great Barrier Reef, including inside mangroves, mangrove edges, and adjacent reef flat areas. The detection range and receiver performance metrics, such as code detection efficiency, rejection coefficient, and noise quotient, were examined and tested among habitats. The results highlighted that the signal from transmitters was successfully detected inside mangrove habitats as well as on the adjacent reef flat. The range to detect at least 50% of transmissions was up to 20 m inside mangroves and up to 120 m outside mangroves. The performance metrics of acoustic receivers inside the mangrove habitat were characterized by low background noise, low rejection rates, and reasonably high code detection efficiency. Furthermore, this study tested the application of this method on juvenile blacktip reef shark Carcharhinus melanopterus and mangrove whipray Urogymnus granulatus, and demonstrated that it can be used to successfully track animals inside mangrove habitat. This novel method could reveal further information on how sharks and rays use mangrove habitats.

Engineered Leukocyte Biomimetic Colorimetric Sensor Enables High-Efficient Detection of Tumor Cells Based on Bioorthogonal Chemistry.

Accurate detection of heterogeneous circulating tumor cells (CTCs) is critical as they can make tumor cells more aggressive, drug-resistant, and metastasizing. Although the leukocyte membrane coating strategy is promising in meeting the challenge of detecting heterogeneous CTCs due to its inherent antiadhesive properties, it is still limited by the reduction or loss of expression of known markers. Bioorthogonal glycol-metabolic engineering is expected to break down this barrier by feeding the cells with sugar derivatives with a unique functional group to establish artificial targets on the surface of tumor cells. Herein, an engineered leukocyte biomimetic colorimetric sensor was accordingly fabricated for high-efficient detection of heterogeneous CTCs. Compared with conventional leukocyte membrane coating, the sensor could covalently bound to the heterogeneous CTCs models fed with Ac4ManNAz in vitro through the synergy of bioorthogonal chemistry and metabolic glycoengineering, ignoring the phenotypic changes of heterogeneous CTCs. Meanwhile, a sandwich structure composed of leukocyte biomimetic layer/CTCs/MoS2 nanosheet was formed for visual detection of HeLa cells as low as 10 cells mL-1. Overall, this approach can overcome the dependence of conventional cell membrane biomimetic technology on specific cell phenotypes and provide a new viewpoint to highly efficiently detect heterogeneous CTCs.

Comparative analysis of AnaLig Sr01 and Sr Resin methods for 210Pb determination in tobacco using TriCarb 3100 TR spectrometry: a statistical approach to accuracy and efficiency

This study aims to statistically compare two methods for determining 210Pb in tobacco: the molecular recognition sorbent method (AnaLig Sr01) and the extraction chromatography sorbent method (Sr Resin). The TriCarb 3100 TR liquid scintillation spectrometer was used to measure and determine 210Pb activity in the tobacco solution. Statistical analysis confirmed the accuracy and precision of the 210Pb measurements, with high detection efficiencies of 82% for both AnaLig Sr01 and Sr Resin. The linear model demonstrated that the residuals followed the Gaussian model, without any autocorrelation or trend. Paired t tests showed consistent results across all methods, and all solid-phase extraction methods proved efficient for 210Pb separations in tobacco samples. These findings provide support for the suitability of both methods and lay a foundation for future articles on tobacco and cigarette samples.

Development of novel intrusion detection in Internet of Things using improved dart game optimizer‐derived optimal cascaded ensemble learning

AbstractBackground of the StudyInternet of things (IoT) industry has accelerated its development with the support of advanced information technology and economic expansion. A complete industrial foundation includes software, chips, electronic components, IoT services, integrated systems, machinery, and telecom operators, which the gradual improvement in the IoT industry system has formulated. As the exponential growth of IoT devices increases, the attack surface available to cybercriminals enables them to carry out potentially more damaging operations. As a result, the security sector has witnessed a rise in cyberattacks. Hackers use several methods to copy and modify the information in the IoT environment. Machine learning techniques are used by the intrusion detection (ID) model to determine and categorize attacks in IoT networks.ObjectivesThus, this study explores the ID system with the heuristic‐assisted deep learning approaches for effectively detect the attacks in the IoT. At first, the IoT data are garnered in benchmark resources. Then, the gathered data is preprocessed to perform data cleaning. Next, the data is transformed and fed to the feature extraction stage. The feature extraction is performed with the help of one‐dimensional convolutional neural network (1D‐CNN), where the features are extracted from the target‐based pooling layer. Then, these attained deep features are fed to the ID phase, where the cascaded ensemble learning (CEL) approach is adopted for detecting the intrusions. Here, the hyperparameter tuning is done with a new suggested improved darts game optimizer (IDGO) algorithm. Here, the main objective of the developed algorithm helps to maximize accuracy in ID.FindingsThroughout the experimental findings, the developed model provides 86% of accuracy. Thus, the finding of the developed model shows less detecting time and higher detection efficiency.

Calculation of efficiency and resolution of a hexagonal NaI(Tl) detector as a function of source position

Most gamma-ray scintillation detectors currently in use are made from inorganic materials that have a relatively high electron density. Quite often they are used to build multidetector systems that provide high scintillation light output. The performance of a gamma radiation detector (its detection efficiency) depends on the shape and size of the crystal, as well as on the source-to-detector geometry used. The NaI(Tl) gamma detector exhibits moderate energy resolution but relatively high gamma-ray detection efficiency and fast time response. In this work, the efficiency and resolution of a scintillation hexagonal detector are studied to optimize its response function. This type and size of scintillator were selected to construct a budget-friendly, reconfigurable, easy-to-maintain multidetector system for registering gamma-rays following fission, capture, and inelastic neutron scattering reactions, with reasonably good energy and time resolutions The research results made it possible to establish a geometric solid angle that increases the efficiency of recording gamma-ray radiation of the hexagonal NaI(Tl) scintillation probe under study.

Development of high-efficiency X-ray detectors based on 1 mm thick monolithic SDD arrays

We present the novel 2× 4 monolithic Silicon Drift Detectors (SDD) array of 1 mm thickness developed within the context of the SIDDHARTA-2 experiment for high-energy X-ray spectroscopy measurements of light kaonic atom transitions. It represents a state-of-the-art advancement in terms of detection efficiency with respect to the previous generation of detectors, having a thickness of 450 μm. The sensor features eight square SDD units with an active area of 8 × 8 mm2 each, arranged in a 2 × 4 matrix. Therefore, the total active area of the array is 32 × 16 mm2 while the total chip area is 36 × 20 mm2, including a 2-mm dead region on each side of the array. This new version of SDD arrays, manufactured by Fondazione Bruno Kessler (FBK), includes an additional electrode on its entrance window, designed to reduce charge sharing between adjacent channels and improve energy resolution. This article describes two different detection modules based on these arrays: the first module includes a single array, whereas the second one is composed of two 1 mm thick SDD arrays in a stacked configuration, in order to reach 2 mm of effective thickness and further increase the module detection efficiency. The first spectroscopic measurements obtained with the two modules will be also reported in the paper, showing the spectroscopic improvements that can be obtained with the additional electrode on the window and the efficiency improvements that can be obtained with the stacked module.

Artificial intelligence assisted tomato plant monitoring system – An experimental approach based on universal multi-branch general-purpose convolutional neural network

Real-time monitoring of tomato plants in plant factories is necessary to identify and classify diseases at early stages to prevent possible outbreaks. The proposed DeepD381v4plus network exhibits higher class-wise accuracy, sensitivity, specificity, precision, F1 score and Matthews correlation coefficient scores exceeding 0.96 for multi-varietal tomato leaf diseases. During the reproductive stage, bud formation, flower appearance, bite marks and fruit set also need to be monitored to confirm pollination. The detector DeepDet381v4 – YOLOv4M achieves the highest mean average precision (mAP) (0.90) and lowest mAP (0.68) in the TFl_Blooming class and the lowest mAP (0.68) in the TFl_Transforming class. However, in real-world simulations, DeepDet381v4 – YOLOv4M can detect and count ripe tomatoes at a distance of 40 cm with little to no error. Both networks used for classification and detection–counting tasks have small sizes with high classification and detection efficiency (>27 fps). Overall, the proposed experimental approach will help farmers prevent disease outbreaks, monitor flower shapes that can set fruits at the highest rate, timely detect and count ripened fruits or recognise damaged fruits due to surface cracks or diseases for harvesting at their optimal maturity stage. This will reduce the labour costs, improve cultivation management and ensure excellent quality of the harvested tomatoes.

Calibration-independent bound on the unitarity of a quantum channel with application to a frequency converter

We report on a method to certify a unitary operation with the help of source and measurement apparatuses whose calibration throughout the certification process needs not be trusted. As in the device-independent paradigm our certification method relies on a Bell test and requires no assumption on the underlying Hilbert space dimension, but it removes the need for high detection efficiencies by including the single additional assumption that non-detected events are independent of the measurement settings. The relevance of the proposed method is demonstrated experimentally by bounding the unitarity of a quantum frequency converter. The experiment starts with the heralded creation of a maximally entangled two-qubit state between a single 40Ca+ ion and a 854 nm photon. Entanglement preserving frequency conversion to the telecom band is then realized with a non-linear waveguide embedded in a Sagnac interferometer. The resulting ion-telecom photon entangled state is assessed by means of a Bell-CHSH test from which the quality of the frequency conversion is quantified. We demonstrate frequency conversion with an average certified fidelity of ≥84% and an efficiency ≥3.1 × 10−6 at a confidence level of 99%. This ensures the suitability of the converter for integration in quantum networks from a trustful characterization procedure.

High throughput compressive fluorescence lifetime imaging with a silicon photomultiplier detector.

Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique for studying biological processes. There exists a growing interest in developing strategies to enhance throughput and reduce acquisition time of FLIM systems, which commonly employ laser scanning excitation and time-correlated single-photon counting (TCSPC) detection. In this work, we propose a wide-field FLIM microscope based on compressive sensing and high photon rate detection (beyond pile-up limit) based on a high-efficiency silicon photomultiplier detector as a single-pixel camera. We experimentally validate the capabilities of this design achieving 20 frames per second FLIM images on free-moving green algae sample.

HeMoDU: High-Efficiency Multi-Object Detection Algorithm for Unmanned Aerial Vehicles on Urban Roads.

Unmanned aerial vehicle (UAV)-based object detection methods are widely used in traffic detection due to their high flexibility and extensive coverage. In recent years, with the increasing complexity of the urban road environment, UAV object detection algorithms based on deep learning have gradually become a research hotspot. However, how to further improve algorithmic efficiency in response to the numerous and rapidly changing road elements, and thus achieve high-speed and accurate road object detection, remains a challenging issue. Given this context, this paper proposes the high-efficiency multi-object detection algorithm for UAVs (HeMoDU). HeMoDU reconstructs a state-of-the-art, deep-learning-based object detection model and optimizes several aspects to improve computational efficiency and detection accuracy. To validate the performance of HeMoDU in urban road environments, this paper uses the public urban road datasets VisDrone2019 and UA-DETRAC for evaluation. The experimental results show that the HeMoDU model effectively improves the speed and accuracy of UAV object detection.

Detection of Multi-Layered Bond Delamination Defects Based on Full Waveform Inversion.

This study aimed to address the challenges encountered in traditional bulk wave delamination detection methods characterized by low detection efficiency. Additionally, the limitations of guided wave delamination detection methods were addressed, particularly those utilizing reflected waves, which are susceptible to edge reflections, thus complicating effective defect extraction. Leveraging the full waveform inversion algorithm, an innovative approach was established for detecting delamination defects in multi-layered structures using ultrasonic guided wave arrays. First, finite element modeling was employed to simulate guided wave data acquisition by a circular array within an aluminum-epoxy bilayer structure with embedded delamination defects. Subsequently, the full waveform inversion algorithm was applied to reconstruct both regular and irregular delamination defects. Analysis results indicated the efficacy of the proposed approach in accurately identifying delamination defects of varying shapes. Furthermore, an experimental platform for guided wave delamination defect detection was established, and experiments were conducted on a steel-cement bilayer structure containing an irregular delamination defect. The experimental results validated the exceptional imaging precision of our proposed technique for identifying delamination defects in multi-layered boards. In summary, the proposed method can accurately determine both the positions and sizes of defects with higher detection efficiency than traditional pulse-echo delamination detection methods.

Dual-Ligand Ruthenium Coordination Polymer-Derived Self-Enhanced Electrochemiluminescent Emitters for Sensitive Detection of Procalcitonin.

Ru-based electrochemiluminescence (ECL) coordination polymers are widely employed for bioanalysis and medical diagnosis. However, commonly used Ru-based coordination polymers face the limitation of low efficiency due to the long distance between the ECL reagent and the coreactant dispersed in detecting solution. Herein, we report a dual-ligand self-enhanced ECL coordination polymer, composed of tris(4,4'-dicarboxylic acid-2,2'-bipyridyl) ruthenium(II) dichloride (Ru(dcbpy)32+) as ECL reactant ligand and ethylenediamine (EDA) as corresponding coreactant ligand into Zn2+ metal node, termed Zn-Ru-EDA. Zn-Ru-EDA shows excellent ECL performance which is attributed to the effective intramolecular electron transport between the two ligands. Furthermore, the dual-ligand polymer allows an anodic low excitation potential (+1.09 V) luminescence. The shift in the energy level of the highest occupied molecular orbital (HOMO) upward after the synthesis of the Zn-Ru-EDA has resulted in a reduced excitation potential. The low excitation potential reduced biomolecular damage and the destruction of the modified electrodes. The ECL biosensor has been constructed using Zn-Ru-EDA with high ECL efficiency for the ultrasensitive detection of a bacterial infection and sepsis biomarker, procalcitonin (PCT), in the range from 1.00 × 10-6 to 1.00 × 10 ng·mL-1 with outstanding selectivity, and the detection limit was as low as 0.47 fg·mL-1. Collectively, the dual-ligand-based self-enhanced polymer may provide an ideal strategy for high ECL efficiency improvement as well as designing new self-enhanced multiple-ligand-based coordination in sensitive biomolecular detection for early disease diagnostics.

Portable electrochemical sensing platform based on amidated GO-MOF and PEDOT:PSS for high-efficient detection of ponceau 4R.

Apromising electrochemical sensing platform for the detection of ponceau 4R in food has been fabricated based on the carboxylated graphene oxide (GO-COOH), metal-organic framework (MOF) UIO-66-NH2, and poly (3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS). To this endGO-COOH was covalently coupled with UIO-66-NH2 through amide reaction, endowing the material (GO-CONH-UIO-66) unique hierarchical pores and high chemical stability and as a result improving the conductivity of MOF and the dispersion of GO. After the addition of PEDOT:PSS into GO-CONH-UIO-66, the continuity and conductivity of the composite (PEDOT:PSS/GO-CONH-UIO-66) have been further enhanced, due to the high conductivity, favorable film-forming, and hydrophilic properties of PEDOT:PSS. Systematic electrochemical experiments confirm that the PEDOT:PSS/GO-CONH-UIO-66/GCE shows satisfactory electrochemical sensing properties towards the detection of ponceau 4R, with a wide linear detection range of 0.01-30μM, a low limit of detection of3.33nM, and a high sensitivity of 0.606 μA μM-1cm-2. ThePEDOT:PSS/GO-CONH-UIO-66 sensing platform was successfully used to detect ponceau 4R in beverage, and the detection results were compared with high-performance liquid chromatography. As a result, the PEDOT:PSS/GO-CONH-UIO-66 composite shows a promising application prospect for rapid detection of ponceau 4R in food and will play significant role in food safety detection and supervision.

Ag-CeO2 Based on Electrochemical Sensor for High-Efficient On-Site Detection of Nitrite in Aquaculture Water and Beverages.

Nitrite is one of the most common nitrogenous compounds, which is not only an important indicator of aquaculture water but also widely used as a food additive. Its potential toxicity poses a huge threat to aquatic products and human health. Therefore, it is important to develop a convenient and rapid sensor for the high-efficient onsite detection of nitrite. In this work, a novel electrochemical sensor was developed for the qualitative and quantitative analysis of nitrite. The developed nitrite electrochemical detection system is easily applied in onsite detection. The electrochemical working electrode was constructed based on the combination of Ag-CeO2 and conductive carbon paste (CPE) with excellent electrocatalysis activity and rapid electron transfer ability. By the application of the developed system and under the optimal conditions, the linear range was from 40.0 μM to 500.0 μM, and the detection limit was reduced to 4.3 μM. The recovery was between 92.1% and 108.1%, and the relative standard deviations (RSDs) were 0.49%~9.31%. The sensor exhibited superior reproducibility, high stability sensitivity, and anti-interference ability, confirming its effectiveness for nitrite analysis. Finally, the developed electrochemical sensor was successfully applied to detect nitrite in beverages and aquaculture water samples, indicating that this approach has great potential in onsite food testing and environmental monitoring.

Inspection of defects in composite structures using long pulse thermography and shearography

Long pulse thermography (LPT) and shearography have been developed as primary methods for detecting debonding or delamination defects in composites due to their full-field imaging, non-contact operation, and high detection efficiency. Both methods utilize halogen lamps as the excitation source for thermal loading. However, the defects detected by the two techniques differ due to their distinct inspection mechanisms. In this study, LPT and shearography are employed to evaluate internal damage in various composite structures. The experimental results demonstrate that LPT, when combined with thermal signal processing algorithms, can clearly detect debonding defects in rubber-to-metal bonded plates, whereas excessive adhesive defects can only be identified by shearography. Flat-bottom holes in the CFRP panel can only be detected by LPT, and shearography is particularly effective for detecting composite materials with a metal skin. For the quantitative measurement of defect sizes, the average errors of the rubber-to-metal bonded plate and CFRP panel using LPT are 4.9 % and 2.2 %, respectively, whereas the average errors of the rubber-to-metal bonded plate and aluminum honeycomb panel using shearography are 15.12 % and 95.4 %, respectively. This indicates that LPT is superior to shearography in quantitatively measuring defect sizes. These two nondestructive testing methods, based on different principles, each have their own advantages and disadvantages. Employing a multi-modal inspection method can leverage their complementary advantages, preventing misdetection and leakage of internal defects in composites.