Precise Detection Research Articles

Intelligent recognition and automatic localization of pipeline welds based on multi-vision system

Abstract Currently, the leakage detection of spacecraft pipeline welds relies on manual point-by-point inspection using a detection gun, which is inefficient and inadequate for the automation needs of spacecraft production. However, the accurate recognition and precise localization of widely distributed and small pipeline welds are crucial for automated detection. Therefore, this paper proposes a multi-vision detection and localization system that integrates global and local information, considering both comprehensive global 3D search and high-precision local 3D measurement. The improved YOLOv8 model is employed for pipeline weld recognition, which improves the recognition rate of welds. Based on the deep learning recognized and segmented welds, this paper proposes stereo matching and segmentation extraction methods for 3D localization and pipeline orientation determination. Additionally, the system integrates a robot to perform automated point-by-point inspection of welds within the area without collisions. The experimental results demonstrate the effectiveness of the improved YOLOv8 and the proposed methods for 3D weld localization and pipeline orientation determination. The maximum deviation of the spatial distance of fine weld positioning is 0.20 mm, and the repeatability of the 3D coordinates is around 0.1 mm. The system can perform precise localization and detection, meeting the requirements for automatic weld recognition and localization.

Enhancing low-level tritium detection in environmental waters: assessing the Hidex ULLA liquid scintillation counter

Tritium (³H) is a vital tracer in isotope hydrology and environmental studies, necessitating accurate and precise low-level detection methods. This study evaluated the performance of the new Hidex ULLA Liquid Scintillation Counter for ultra-low-level tritium assays in environmental water samples. The optimization of instrumental parameters enabled accurate and reproducible 3H measurements. Test samples (10–132 TU) were analyzed in triplicate, including 3H-free water and NIST traceable standards. The results demonstrated excellent accuracy and reproducibility, as evidenced by Zeta scores and linearity across these tritium concentrations. The system's stability over extended measurement time periods ensured reliable performance for routine ultra-low level 3H analysis. The optimization of key parameters such as coincidence time and region of interest (ROI) achieved the highest figure of merit (FoM) by using a 25 ns coincidence time and the DigPb open mode for optimal spectral fittings. Detection limits (DL) based on ISO11929 recommendations were significantly improved but highlighted the need for sample enrichment for most low-level environmental applications, with LSC counting times preferably exceeding 1000 min. Overall, the Hidex ULLA Liquid Scintillation Counter proved to be a robust and reliable instrument suitable for ultra-low-level tritium detection in environmental water samples.

Direct Current Ripple Component Detection Algorithm Based on Improved Variational Mode Decomposition

Background: The development of smart grids requires energy meters that enable accurate measurements. Objective: In response to the current issue where Direct Current (DC) energy meters only measure DC components and not ripple components, we propose to design a novel algorithm to achieve accurate separation of DC components and ripple components in DC signals. Methodology: This patent proposes a precise detection algorithm for ripple components. The algorithm is based on the Wavelet Decomposition combined with Flamingo Search Algorithm-Variational Mode Decomposition (WD-FSA-VMD). Firstly, we analyze the ripple characteristics in the DC signal and use the Wavelet Decomposition (WD) algorithm to separate the ripple components in the DC signal accurately. Secondly, we utilize the Flamingo Search Algorithm (FSA) to optimize the number of modal decompositions and the penalty factor in the Variational Mode Decomposition (VMD). This allows us to accurately extract the ripple components. Finally, we conducted simulation experiments comparing the improved algorithm with the original algorithm. Results: The experimental results indicate that the signal correlation coefficient obtained by the WDFSA- VMD algorithm increases by 82.64% compared to traditional VMD, and it increases by 16.72% compared to the combined Wavelet Decomposition with Whale Optimization Algorithm-Variational Mode De-composition (WD-WOA-VMD). Conclusion: The improved algorithm we proposed has good adaptability and separation performance. It is prospective for the new algorithm to provide a valuable reference to ripple detection technology.

Lightweight Detection of Broccoli Heads in Complex Field Environments Based on LBDC-YOLO

Robotically selective broccoli harvesting requires precise lightweight detection models to efficiently detect broccoli heads. Therefore, this study introduces a lightweight and high-precision detection model named LBDC-YOLO (Lightweight Broccoli Detection in Complex Environment—You Look Only Once), based on the improved YOLOv8 (You Look Only Once, Version 8). The model incorporates the Slim-neck design paradigm based on GSConv to reduce computational complexity. Furthermore, Triplet Attention is integrated into the backbone network to capture cross-dimensional interactions between spatial and channel dimensions, enhancing the model’s feature extraction capability under multiple interfering factors. The original neck network structure is replaced with a BiFPN (Bidirectional Feature Pyramid Network), optimizing the cross-layer connection structure, and employing weighted fusion methods for better integration of multi-scale features. The model undergoes training and testing on a dataset constructed in real field conditions, featuring broccoli images under various influencing factors. Experimental results demonstrate that LBDC-YOLO achieves an average detection accuracy of 94.44% for broccoli. Compared to the original YOLOv8n, LBDC-YOLO achieves a 32.1% reduction in computational complexity, a 47.8% decrease in parameters, a 44.4% reduction in model size, and a 0.47 percentage point accuracy improvement. When compared to models such as YOLOv5n, YOLOv5s, and YOLOv7-tiny, LBDC-YOLO exhibits higher detection accuracy and lower computational complexity, presenting clear advantages for broccoli detection tasks in complex field environments. The results of this study provide an accurate and lightweight method for the detection of broccoli heads in complex field environments. This work aims to inspire further research in precision agriculture and to advance knowledge in model-assisted agricultural practices.

Real-Time Detection and Localization of Weeds in Dictamnus dasycarpus Fields for Laser-Based Weeding Control

Traditional Chinese medicinal herbs have strict environmental requirements and are highly susceptible to weed damage, while conventional herbicides can adversely affect their quality. Laser weeding has emerged as an effective method for managing weeds in precious medicinal herbs. This technique allows for precise weed removal without chemical residue and protects the surrounding ecosystem. To maximize the effectiveness of this technology, accurate detection and localization of weeds in the medicinal herb fields are crucial. This paper studied seven species of weeds in the field of Dictamnus dasycarpus, a traditional Chinese medicinal herb. We propose a lightweight YOLO-Riny weed-detection algorithm and develop a YOLO-Riny-ByteTrack Multiple Object Tracking method by combining it with the ByteTrack algorithm. This approach enables accurate detection and localization of weeds in medicinal fields. The YOLO-Riny weed-detection algorithm is based on the YOLOv7-tiny network, which utilizes the FasterNet lightweight structure as the backbone, incorporates a lightweight upsampling operator, and adds structure reparameterization to the detection network for precise and rapid weed detection. The YOLO-Riny-ByteTrack Multiple Object Tracking method provides quick and accurate feedback on weed identification and location, reducing redundant weeding and saving on laser weeding costs. The experimental results indicate that (1) YOLO-Riny improves detection accuracy for Digitaria sanguinalis and Acalypha australis, ultimately amounting to 5.4% and 10%, respectively, compared to the original network. It also diminishes the model size by 2 MB and inference time by 10 ms, making it more suitable for resource-constrained edge devices. (2) YOLO-Riny-ByteTrack enhances Multiple Object Tracking accuracy by 3%, reduces ID switching by 14 times, and improves overall tracking accuracy by 3.4%. The proposed weed-detection and localization method for Dictamnus dasycarpus offers fast detection speed, high localization accuracy, and stable tracking, supporting the implementation of laser weeding during the seedling stage of Dictamnus dasycarpus.

Multi-resolution visual Mamba with multi-directional selective mechanism for retinal disease detection.

Retinal diseases significantly impact patients' quality of life and increase social medical costs. Optical coherence tomography (OCT) offers high-resolution imaging for precise detection and monitoring of these conditions. While deep learning techniques have been employed to extract features from OCT images for classification, convolutional neural networks (CNNs) often fail to capture global context due to their focus on local receptive fields. Transformer-based methods, on the other hand, suffer from quadratic complexity when handling long-range dependencies. To overcome these limitations, we introduce the Multi-Resolution Visual Mamba (MRVM) model, which addresses long-range dependencies with linear computational complexity for OCT image classification. The MRVM model initially employs convolution to extract local features and subsequently utilizes the retinal Mamba to capture global dependencies. By integrating multi-scale global features, the MRVM enhances classification accuracy and overall performance. Additionally, the multi-directional selection mechanism (MSM) within the retinal Mamba improves feature extraction by concentrating on various directions, thereby better capturing complex, orientation-specific retinal patterns. Experimental results demonstrate that the MRVM model excels in differentiating retinal images with various lesions, achieving superior detection accuracy compared to traditional methods, with overall accuracies of 98.98\% and 96.21\% on two public datasets, respectively. This approach offers a novel perspective for accurately identifying retinal diseases and could contribute to the development of more robust artificial intelligence algorithms and recognition systems for medical image-assisted diagnosis.

Detection of tea leaf blight in UAV remote sensing images by integrating super-resolution and detection networks.

Tea leaf blight (TLB) is a common disease of tea plants and is widely distributed in tea gardens. Although the use of unmanned aerial vehicle (UAV) remote sensing can help to achieve a wider scale for TLB detection, the blurring of UAV images, overlapping of tea leaves, and small size of TLB spots pose significant challenges to the task of detection. This study proposes a method of detecting TLB in UAV remote sensing images by integrating super-resolution (SR) and detection networks. We use an SR network called SERB-Swin2sr to reconstruct the detailed features of UAV images and solve the problem of detail loss caused by the blurring in UAV images. In SERB-Swin2sr, a squeeze-and-excitation ResNet block (SERB) is introduced to enhance the models' ability to extract the target details in the images, and the convolution stem replaces the convolution block in order to increase the convergence rate and stability of the network. A detection network called SDDA-YOLO is applied to achieve precise detection of TLB in UAV remote sensing images. In SDDA-YOLO, a shuffle dual-dimensional attention (SDDA) module is introduced to enhance the feature fusion capability of the network, and an Xsmall-scale detection layer is used to enhance the detection ability of small lesions. Experimental results show that the proposed method is superior to current detection methods. Compared with a baseline YOLOv8 model, the precision, mAP@0.5, and mAP@0.5:0.95 of the proposed method are improved by 4.2%, 1.6%, and 1.8%, and the size of our model is only 4.6MB.

Sequentially Activated-Dumbbell DNA Nanodevices for Accurate Detection of Uracil-DNA Glycosylase via PER-Based Orthogonal Signal Outputs.

Accurate and reliable detection of uracil-DNA glycosylase (UDG) activity is crucial for clinical diagnosis and prognosis assessment. However, current techniques for accurately monitoring UDG activity still face significant challenges due to the single input or output signal modes. Here, we develop a sequentially activated-dumbbell DNA nanodevice (SEAD) that enables precise and reliable evaluation of UDG activity through primer exchange reactions (PER)-based orthogonal signal output. The SEAD incorporates a double-hairpin structure with a stem containing two deoxyuridine (dU) sites for target recognition and two preblocked primer binding regions for target amplification and signal output. Upon UDG recognition of dU, the SEAD can be cleaved by apurinic/apyrimidinic endonuclease 1 (APE1), generating two different hairpins with exposed primer binding regions. These hairpins serve as templates to initiate the parallel PER, enabling the extending of two different amplification products: a long single-stranded DNA (ssDNA) with repetitive sequences and a short ferrocene-labeled ssDNA with complementary sequences. These products further self-assemble into DNA nano-strings in an orthogonal manner that act as an electrochemiluminescence signal switch, enabling precise detection of low-abundance UDG. This work develops a sequential input and orthogonal output strategy for accurately monitoring UDG activity, highlighting the significant potential in cancer diagnosis and treatment.

Spectral data driven machine learning classification models for real time leaf spot disease detection in brinjal crops

This study presents the development and evaluation of machine learning models for detecting leaf spot disease in brinjal crops using spectral sensor data. The spectral reflectance of diseased and healthy tissues was recorded across nine wavelength bands (F1: 415 nm, F2: 445 nm, F3: 480 nm, F4: 515 nm, F5: 555 nm, F6: 590 nm, F7: 630 nm, F8: 680 nm, and F9: NIR-750 nm). The data revealed distinct spectral signatures, particularly between F5 (555 nm) and F9 (NIR), where diseased tissues consistently showed lower reflectance compared to healthy tissues. Two machine learning algorithms, Decision Tree (DT) and Support Vector Machine (SVM), were employed to classify the spectral data. The DT model achieved a maximum testing accuracy of 88.2 %, with a Gini index and a depth of 4 as optimal hyperparameters. The confusion matrix indicated that the DT model correctly identified 883 diseased instances and 667 healthy cases, while misclassifying 213 healthy tissues as diseased and 25 diseased tissues as healthy. The SVM model, configured with a cost parameter of 10.0 and a tolerance of 0.01, outperformed the DT model, achieving a testing accuracy of 92.4 %. The SVM model correctly classified 99.3 % of diseased instances and 94.1 % of healthy cases. The results demonstrate the potential of spectral sensor data combined with ML algorithms for precise disease detection, facilitating targeted pesticide application, and reducing input costs. The high accuracy of the SVM model underscores its utility in agricultural disease management, enabling early intervention and enhancing crop health monitoring. Future research may explore integrating multiple sensors and advanced feature extraction methods to further improve the efficiency and accuracy of these systems.

DNA logic gate for cancer cells identification, detection, and modulation of cell–cell interactions

The precise detection of cancer cells and modulation of cell–cell interactions would be highly desired for noninvasive and nontoxic cancer treatments but it remains challenging due to the existence of biomarkers on both normal and tumor cells. In this work, we engineered a logic-gated DNA molecular machine incorporating a membrane protein aptamer and an extracellular acid responsive split i-motif for the precise identification and detection of cancer cells, as well as the mediation of cell–cell interactions. In this system, only in the presence of both the membrane protein and protons, the logic-gated DNA molecular machine is activated to perform the “AND” logic operation to induce catalytic hairpin assembly (CHA) on the electrode, leading to the formation of G-quadruplex/hemin and the generation of an amplified electrochemical signal. On the other hand, the formed “AND” logic could hybridize with the DNA fragment on T-cell, effectively shorting the distance between T-cell and cancer cells. Benefiting from the DNA logic gate and CHA, this strategy could not only precisely identify cancer cells, but also show a sensitive analysis of cancer cells with a detection limit of 15 cells/mL. The logic-gated DNA molecular machine achieved the identification of cancer cells and modulation of intercellular interactions, providing a new idea for precise disease diagnosis and therapy.

Defect detection using integration of ultrasonic least-squares reverse time migration and generative adversarial network

ABSTRACT Accurate detection and characterisation of defects in high-density polyethylene (HDPE) materials are important for the safety of industrially critical structures. Ultrasonic non-destructive evaluation (UNDE) has proven to be a powerful tool for detecting and characterising defects in engineered materials. However, efficient and high-precision defect imaging in these highly attenuating materials remains a significant challenge for UNDE. Least-squares reverse time migration (LSRTM) offers the potential to reconstruct high-precision images of reflectivity. Yet, the conventional LSRTM iteratively updates the reflectivity model by minimising the data residuals, making it computationally expensive. In this paper, an efficient ultrasonic LSRTM algorithm within a deep learning framework is proposed. Building upon this, a generative adversarial network (GAN) is integrated to further enhance the reconstruction results by reducing artefacts in the images. Simulation and experimental results show that the proposed ultrasonic LSRTM-GAN can generate high-quality images, effectively enabling precise defect detection in HDPE.

Dual-electrode signal amplification self-powered biosensing platform based on nanozyme boosting target-induced DNA nanospace array for ultrasensitive detection of sugarcane Pokkah Boeng disease pathogenic bacteria

Sugarcane is a crop with significant economic importance worldwide. However, pokkah boeng disease poses a serious threat to its production and the sustainable development. There is a pressing necessity for precise and portable detection methods. We develop a dual-electrode signal amplification biosensing platform, for highly sensitive detection of sugarcane pokkah boeng disease pathogenic bacteria. This innovative platform integrates highly catalytic AuNPs/Mn3O4 nanozymes with N-GDY, along with a target-induced development of DNA nanostructure arrays. AuNPs/N-GDY serves as dual electrode substrates, and AuNPs/Mn3O4 nanozymes are surface-loaded as the bioanode. The biocathode is constructed by introducing DNA nanospace arrays onto the electrode through target-induced methods. [Ru(NH3)6]3+ is embedded into the nucleic acid double-helix scaffold via electrostatic adsorption, generating an EOCV signal that is strongly correlated with the target concentration. To further enhance sensitivity, the detection platform is combined with a capacitor to amplify the detection signal, utilizing its high power density, which results in a 22.5-fold increase in sensitivity. The method offers a linear detection range of 0.0001 to 10,000 pM and an detection limit of 32.5 aM (S/N = 3). This method supplies a novel approach for real-time monitoring and competent oversight of pokkah boeng disease pathogenic bacteria.

Plasmonic Coupling for High‐Sensitivity Detection of Low Molecular Weight Molecules

This article presents a novel plasmonic sensing platform designed for the detection of low molecular weight molecules, offering significant advancements in diagnostic applications. The platform features a periodic array of gold nanodisks on a 20 nm thin silica layer, supported by a 100 nm thick gold substrate. By leveraging the coupling between localized and propagating surface plasmon resonances, this design significantly enhances the sensitivity and specificity of molecular detection. Finite element method simulations are conducted to characterize the optical properties and reflectance response of the nanodisks array in the visible to near‐infrared range. Ellipsometric analysis is performed to measure the reflectance of the sample at various angles. Additionally, scanning near‐field optical microscopy in reflectance mode validates the design by revealing well‐defined plasmonic hot spots and interference patterns consistent with the simulated results. The findings demonstrate the platform's effectiveness in amplifying optical signals, achieving a limit of detection of 50 μM for molecules with a molecular weight of less than 1 KDa. This high sensitivity and specificity highlight the potential of the proposed plasmonic platform to advance the development of highly sensitive sensors for low molecular weight molecules, making it a valuable tool for diagnostics and precise molecular detection.

Transformative personalized oxalate biomarker analysis through a wrist-worn microneedle device integrated with duplex nanozyme toolbox

The interstitial fluid (ISF) is valuable for disease diagnosis due to its unique biomarkers. Microneedles (MNs) offer a painless method for ISF sampling, facilitating primary diagnostics. This study introduces a minimally invasive, biocompatible MN patch made from a swellable hydrogel of polyvinyl alcohol (PVA) and polyvinyl pyrrolidone (PVP), which effectively extracts ISF. The hydrogel selectively permits small molecules like oxalate, a kidney disease biomarker, while blocking large biomolecules. The MN patch is integrated with a multilayer sensor via microfluidic channels, featuring cotton fiber, agarose, and Mn3[Fe(CN)6]2, alongside Fe3[Fe(CN)6]3 (AGH-CP/MnFeCN/FeFeCN). These nanozymes exhibit oxidase-like activity, reacting with 3,3′,5,5′-tetramethylbenzidine (TMB) to indicate oxalate levels through a color change from green (no oxalate) to light or dark blue, based on concentration. The designed sensor demonstrates high performance with a detection limit of 0.897 μM and an RSD of 1.22 %, enabling accurate quantification via smartphone image analysis as readout platforms for more accessibility and user-friendliness which helps to reach personalized healthcare goals. This integration facilitates real-time, remote, precise disease detection, biochemical analysis and forsooth miniaturization, leading to the development of portable, flexible, and wearable sensors. The MN patch and integrated sensor show excellent stability, selectivity, sensitivity, reliability, and long-term durability. This minimally invasive sensing system, designed for wristband integration, opens new avenues for precision point-of-care monitoring, enhancing personal healthcare.

Nonlinear enhanced mass sensor based on optomechanical system

Abstract A high-precision and tunable mass detection scheme based on a double-oscillator optomechanical system is proposed. By designating one of the oscillators as the detection port, tiny mass signals can be probed through the frequency shift of the output spectrum, utilizing the system's optomechanically induced transparency (OMIT) effect. By solving the output of the optical mode, we demonstrate that the system exhibits two OMIT windows due to the double oscillator coupling, with one window being strongly dependent on the mass to be detected. Characterizing the spectrum around this window enables high magnification and precise detection of the input signal under nonlinear parameter conditions. Additionally, our scheme shows resilience to environmental temperature variations and drive strength perturbations.

High-precision and lightweight small-target detection algorithm for low-cost edge intelligence

The proliferation of edge devices driven by advancements in Internet of Things (IoT) technology has intensified the challenge of achieving high-precision small target detection, as it demands extensive computational resources. This amplifies the conflict between the need for precise detection and the requirement for cost-efficiency across numerous edge devices. To solve this problem, this paper introduces an enhanced target detection algorithm, MSGD-YOLO, built upon YOLOv8. The Faster Implementation of CSP Bottleneck with 2 convolutions (C2f) module is enhanced through the integration of the Ghost module and dynamic convolution, resulting in a more lightweight architecture while enhancing feature generation. Additionally, Spatial Pyramid Pooling with Enhanced Local Attention Network (SPPELAN) replaces Spatial Pyramid Pooling Fast (SPPF) to expand the receptive field, optimizing multi-level feature aggregation for improved performance. Furthermore, a novel Multi-Scale Ghost Convolution (MSGConv) and Multi-Scale Generalized Feature Pyramid Network (MSGPFN) are introduced to enhance feature fusion and integrate multi-scale information. Finally, four optimized dynamic convolutional detection heads are employed to capture target features more accurately and improve small target detection precision. Evaluation on the VisDrone2019 dataset shows that compared with YOLOv8-n, MSGD-YOLO improves mAP@50 and mAP@50–95 by 14.1% and 11.2%, respectively. In addition, the model not only achieves a 16.1% reduction in parameters but also attains a processing speed of 24.6 Frames Per Second (FPS) on embedded devices, thereby fulfilling real-time detection requirements.

A HOF-101@AgNPs-based dual-signal mode aptasensor for electrochemiluminescence and fluorescence detection of E. coli O157:H7

Escherichia coli O157:H7 (E. coli O157:H7) emerges as a foodborne pathogen, emphasizing the imperative for creating precise detection tools. An ultrasensitive electrochemiluminescence/fluorescence (ECL/FL) dual-signal mode aptasensor was constructed for the detection of E. coli O157:H7. Ultrafine silver nanoparticles were loaded on the surface of hydrogen-bonded organic skeleton materials by in-situ photoreduction method, and then combined with aptamers that can identify specific targets to prepare HOF-101@AgNPs@Apt, greatly simplifying the ECL/FL dual-signal mode probe fabrication process and improving sensing reliability. HOF-101@AgNPs@Apt had both strong ECL luminescence and fluorescence, resulting in a high detection sensitivity. The low limit of detection (LOD) for ECL and FL were 0.48 CFU mL−1 and 2.39 CFU mL−1, respectively. Moreover, the proposed dual-signal mode aptasensor has been successfully applied to determine E. coli O157:H7 levels in tap water and milk with superior accuracy and high antiinterference capability, providing a promising method for food safety monitoring.

Design and Simulation of a Microcantilever Sensor for Precise Detection of Volatile Organic Compounds

Design and Simulation of a Microcantilever Sensor for Precise Detection of Volatile Organic Compounds

Modelling and Analysis of Advanced Dynamic Learning Techniques and Anisotropic Diffusion Filtering for Automated Crack Detection in Civil Structures

This study introduces a cutting-edge automated system that leverages dynamic learning methods for detecting cracks in civil structures. By combining pioneering preprocessing techniques with leading-edge machine learning algorithms, the proposed system aims to surpass the constraints of conventional manual inspection approaches. The methodology incorporates Anisotropic Diffusion Non-Local Mean Filtering (ADNLMF) for noise reduction and YOLO v3 for precise object detection. The system's effectiveness is substantiated using an extensive dataset, displaying substantial enhancements in both accuracy and efficiency.

ISETECH-Analysis Detection of Real-Time Metallic Surface Defect using MobileNetv2 and YOLOv3 on Raspberry Pi

This work presents an innovative solution utilizing a Raspberry Pi detection system to identify any defects on metallic surfaces in real-time. Manual inspection has several limitations, including time-consuming, subjective assessments, and a higher probability of human error could compromise product quality, lead to potential failures, and result in substantial costs for manufacturers. The primary focus of this endeavour is to enhance manufacturing efficiency and reduce labour expenses by automating the defect identification process. This objective is realized by employing the YOLOv3-tiny and MobileNetv2 algorithms which are subsequently deployed on a Raspberry Pi to enable precise and swift defect detection on metallic surfaces. The implementation process involves training and testing the models on a computer, followed by their deployment onto the Raspberry Pi. Upon proper setup, the trained models are employed for real-time inferences, effectively identifying defects. Notably, while the MobileNetv2 exhibits impressive accuracy in classifying defect types above 0.9, it is found to be less efficient for real-time detection on the Raspberry Pi. In contrast, the YOLO model proves to be well-suited for real-time detection on this platform with above probability of 0.8 for selected types of defects. The successful integration of this model significantly transforms quality control and inspection procedures across various industries.