Defect Detection Research Articles

Efficient packaging defect detection: leveraging pre-trained vision models through transfer learning

The inspection of packaging defects is a crucial aspect of maintaining the quality of industrial production, especially in the case of boxed products. This study introduces a novel approach for detecting physical defects in product packaging boxes by integrating image processing with deep learning, specifically transfer learning with two images as an input. The proposed method utilizes both top view and side view images of the packaging to determine its condition, a significant departure from the conventional single image input. Our approach incorporates 16 pre-trained model variants from EfficientNetV2, MobileNetV3, and ResNetV2 for transfer learning as feature extractors. The experimental findings demonstrate that the best model that leverages EfficientNetV2 variant achieves 100% accuracy and F1 score in terms of classification performance. However, the most optimal model in terms of classification performance and inference speed was the one that leveraged ResNetV2 variant. This model scored 95% accuracy and 95.24% F1 score, with an inference speed of 91 ms per prediction.

Towards an optimization of automatic defect detection by artificial neural network using Lamb waves

This paper presents a damage detection method based on the inverse pattern recognition technique by artificial neural network (ANN) using ultrasonic waves. Lamb waves are guided elastic waves, are widely employed in nondestructive testing thanks to their attractive properties such as their sensitivity to the small defects. In this work, finite element method was conducted by Abaqus to study Lamb modes propagation. A data collection is performed by the signals recorded from the sensor of 300 models: healthy and damaged plates excited by a tone burst signal with the frequencies: 100 kHz, 125 kHz, 150 kHz, 175 kHz, 200 kHz, and 225 kHz. The captured signals in undamaged plat are the baseline, whereas the signals measured in damaged plates are recorded for various positions of external rectangular defects. To reduce the amount of training data, only two peaks of measured signals are required to be the input of the model. Continuous wavelet transform (CWT) was adopted to calculate the key features of the signal in the time domain. The feed forward neural network is implemented using MATLAB program. The data are divided as follows: 70% for training the model, 25% for the validation, and 5% for the test. The proposed model is accurate estimating the position of the defect with an accuracy of 99.98%.

IoT Power Optimization: A Comprehensive Survey

Abstract: The Receiving station fault detection correction has been a goal of power system engineers to achieve maximum required Receiving side Power from the generating station , since the creation of distribution and transmission systems. By enabling the disconnecting of faulty lines prior to any serious equipment damage. Rapid fault detection can help to protect the machinery. An accurate fault type can be used to determine the maximum power of the generation station. Various algorithms are being developed to perform this task with greater accuracy and efficiency. The cloud IOT server receives these fault condition and solution data for immediate response. It is necessary to identify issues with power transmission lines for the security and maintenance of a power system. Most defect detection methods use the electrical quantities that current and voltage transformers provide for measurement. These transformers can be expensive and must come into direct contact with the highvoltageequipment being observed.

Achieving operational excellence: Key factors in total quality management for pharmaceutical industry

The implementation of total quality management always concerned by top management within organization. TQM is the process of involving all employees of organization in its continual improvement to achieve customer satisfaction. The aim of this study is focused at elements & key success factors implemented TQM process. Quality is critically important ingredient to organizational success achieved by TQM, which aimed at prevention of defects rather than detection of defects.

Radial Field Detector to Improve Sensitivity of Localized Defects in Remote Field Eddy Current Technique

ABSTRACT This paper presents the development of a cone-shaped radial magnetic-field-detector–based remote field eddy current (RFEC) probe for enhanced detection of localized defects in ferritic steel tubes. The performance of the radial coil detector has been evaluated for the detection of different types of defects such as through holes, flat bottom holes and notches of different sizes and orientations. The RFEC signals are observed to be corrupted by heavy noise due to spatial variations in material permeability. To remove the random noise from the nonstationary time series RFEC signals, empirical mode decomposition (EMD) processing has been adopted and has been found to significantly improve the signal-to-noise (SNR) ratio. Furthermore, the effect of the circumferential extent of defects on the signal amplitude is analyzed through experimental and finite-element-model–based studies. The linear resolution of the designed radial coil detector and its repeatability behavior are also scrutinized. Studies demonstrate that the developed radial field detector is a promising configuration in remote field eddy current technique for enhanced defect detection and also in designing an array probe.

ANALYSIS OF SURFACE QUALITY IN BASE ACRYLIC PLASTIC USING FLAW DETECTION METHOD

Before scanning the surface of the base acrylic plastic, ten samples were selected with a surface that had undergone all stages of finishing according to the proper dental protocol and ten samples that had previously been artificially damaged with scratches using sandpaper. This study addresses the assessment of the quality of removable orthopedic appliances concerning surface polishing and detection of microdefects. The research employs a comparative analysis of reference samples of base plastic and artificially damaged samples using flaw detection analysis. Prior to scanning the surface of the base acrylic plastic, ten samples underwent all stages of finishing according to dental protocol, while another ten samples were intentionally damaged with scratches using sandpaper. The macrophotographs obtained were processed using the "Image J" program, allowing for quantitative analysis of images, enabling the evaluation of surface quality through various parameters. This included evaluating the total number of defects, as well as the average area and perimeter of the defects. Statistical analysis conducted in the JUSP program enabled comparison between the two groups of samples, identifying significant quantitative differences. Based on the prominent indicators available in the Image J program, conclusions were drawn regarding the quality of the acrylic plastic surface and the factors influencing its condition through specific mechanical defects. The proposed method of light-optical flaw detection expands the clinical assessment capabilities of removable orthopedic structures (base plastic), particularly concerning potential bacterial contamination, which can serve as a significant factor in the formation of inflammation foci on the mucous membrane of the denture-supporting oral tissues. Flaw detection analysis enables a comprehensive assessment of material surface polishing quality and the detection of surface defects that may arise during production or use, thus preventing biofilm accumulation. The experiment demonstrated the effectiveness of light-optical flaw detection and highlights the potential for its further improvement and broader implementation in clinical practice.

A Multi-Information Fusion Method for Repetitive Tunnel Disease Detection

Existing tunnel defect detection methods often lack repeated inspections, limiting longitudinal analysis of defects. To address this, we propose a multi-information fusion approach for continuous defect monitoring. Initially, we utilized the You Only Look Once version 7 (Yolov7) network to identify defects in tunnel lining videos. Subsequently, defect localization is achieved with Super Visual Odometer (SuperVO) algorithm. Lastly, the SuperPoint–SuperGlue Matching Network (SpSg Network) is employed to analyze similarities among defect images. Combining the above information, the repeatability detection of the disease is realized. SuperVO was tested in tunnels of 159 m and 260 m, showcasing enhanced localization accuracy compared to traditional visual odometry methods, with errors measuring below 0.3 m on average and 0.8 m at maximum. The SpSg Network surpassed the depth-feature-based Siamese Network in image matching, achieving a precision of 96.61%, recall of 93.44%, and F1 score of 95%. These findings validate the effectiveness of this approach in the repetitive detection and monitoring of tunnel defects.

An accurate object detection of wood defects using an improved Faster R-CNN model

ABSTRACT Wood defect classification and location are crucial for automatic repair and reuse in wood product manufacture, which requires high accuracy and efficiency in the engineering practice. In this paper, a novel modeling method is proposed for faster R-CNN to improve the object detection of wood defect. An improved ResNet-50 network, a focal loss function, and a soft non-maximum suppression (soft NMS) were adopted to replace the counterparts of the initial Faster R-CNN. Experiments were conducted on a wood defect dataset with seven types of defects. The experimental results indicate that the using of the improved ResNet-50 network brings a significant increase in mean average precision (mAP) by 4.4% and reduces the detection time by 3.6% than that of the traditional Faster R-CNN on wood defect detection. Besides, mAPs are 3.4% and 2.8% higher than that of the traditional Faster R-CNN as the employment of the focal loss function and soft NMS, respectively. Finally, the proposed model obtains better mAP than the classic target detection networks. Results demonstrate the effectiveness and feasibility of the proposed modeling method in engineering applications.

Effect of the void and crack quantity and location on ultrasound: resin experiments and carbon brick validation

ABSTRACT In industrial applications, there is a lack of methods applicable to large-scale, rapid and non-destructive detection of voids and cracks inside carbon bricks. This paper used the ultrasonic testing (UT) A-scan method to investigate the influence of quantity (void fraction:1.4%-4.6%; number of artificial cracks:1–7) and location (five layers) of voids and cracks inside the resin on ultrasonic waves. Both fast Fourier transform (FFT) and synchrosqueezed wavelet transform (SWT) are employed to analyse the amplitude, main frequency and normalised peak time of ultrasonic waves. The results indicate that increased void fraction and number of cracks lead to greater amplitude attenuation, promoting the transition from the fundamental wave to harmonics. Void and crack location symmetrically affect ultrasound amplitude, main frequency, and normalised peak time around the sample centre. Meanwhile, fitting equations are established for the main frequency of the fundamental wave (f 1) in relation to defect quantity. A high-precision quantitative defects detection standard is also developed based on normalised peak time. The validation based on carbon bricks indicates that the derived empirical relations between these parameters and defects can lay the foundation for large-scale and rapid non-destructive detection in carbon bricks.

Identification Methods for Modal Parameters of Track Structures and Their Application Status and Prospects

Rail transit’s wheel–rail system periodically encounters defects such as wheel polygons, rail corrugation, and rail fastener failure, which are intricately linked to the modal parameters of track structures. Identifying these modal parameters is essential for refining wheel–rail dynamics models, understanding track defect mechanisms, and defect detection. This study reviews the current methodologies for identifying track structure modal parameters, emphasizing their significance in track engineering. It categorizes various identification techniques, examines their development, and highlights their application in updating track dynamics theoretical models. The relationship between track modal parameters and wheel–rail defects is discussed, alongside a summary of modal parameter-based defect remediation strategies globally. The paper also evaluates the current state of defect identification research utilizing track modal parameters. In the “prospects” section, three forward-looking research avenues are proposed. These approaches are poised to streamline and improve the efficiency of modal parameter extraction, marking potential breakthroughs in the field.

AdvancingTire Safety: Explainable Artificial Intelligence-Powered Foreign Object Defect Detection with Xception Networks and Grad-CAM Interpretation

Automatic detection of tire defects has become an important issue for tire production companies since these defects cause road accidents and loss of human lives. Defects in the inner structure of the tire cannot be detected with the naked eye; thus, a radiographic image of the tire is gathered using X-ray cameras. This image is then examined by a quality control operator, and a decision is made on whether it is a defective tire or not. Among all defect types, the foreign object type is the most common and may occur anywhere in the tire. This study proposes an explainable deep learning model based on Xception and Grad-CAM approaches. This model was fine-tuned and trained on a novel real tire dataset consisting of 2303 defective tires and 49,198 non-defective. The defective tire class was augmented using a custom augmentation technique to solve the imbalance problem of the dataset. Experimental results show that the proposed model detects foreign objects with an accuracy of 99.19%, recall of 98.75%, precision of 99.34%, and f-score of 99.05%. This study provided a clear advantage over similar literature studies.

Robust anomaly detection in industrial images by blending global–local features

AbstractIndustrial image anomaly detection achieves automated detection and localization of defects or abnormal regions in images through image processing and deep learning techniques. Currently, utilizing the approach of reverse knowledge distillation has yielded favourable outcomes. However, it is still a challenge in terms of the feature extraction capability of the image and the robustness of the decoding of the student network. This study first addresses the issue that the teacher network has not been able to extract global information more effectively. To acquire more global information, a vision transformer network is introduced to enhance the model's global information extraction capability, obtaining better features to further assist the student network in decoding. Second, for anomalous samples, to address the low similarity between features extracted by the teacher network and features restored by the student network, Gaussian noise is introduced. This further increases the probability that the features decoded by the student model match normal sample features, enhancing the robustness of the student model. Extensive experiments were conducted on industrial image datasets AeBAD, MvtecAD, and BTAD. In the AeBAD dataset, under the PRO performance metric, the result is 89.83%, achieving state‐of‐the‐art performance. Under the AUROC performance metric, it reaches 83.35%. Similarly, good results were achieved on the MvtecAD and BTAD datasets. The proposed method's effectiveness and performance advantages were validated across multiple industrial datasets, providing a valuable reference for the application of industrial image anomaly detection methods.

Smart defect detection using transfer learning in injection molding: a comparative exploration study of deep learning architectures

Smart defect detection using transfer learning in injection molding: a comparative exploration study of deep learning architectures

Generative Model-Driven Synthetic Training Image Generation: An Approach to Cognition in Railway Defect Detection

Generative Model-Driven Synthetic Training Image Generation: An Approach to Cognition in Railway Defect Detection

Fresh Fruit Detection Using Deep Learning

The Fresh Fruit Identification System constitutes a notable advancement in the meals industry. Employing sophisticated technologies along with machine-getting-to-know and computer imagination and prescient, it complements the great evaluation and optimization of perishable items, mainly focusing on culmination and greens. This contemporary system employs advanced gadgets gaining knowledge of models, especially convolutional neural networks (CNNs), to meticulously analyze high-decision pics of processed meals. By rapidly and appropriately identifying objects based totally on attributes that include shade, texture, and shape, the machine guarantees no longer the simplest precision in categorization but also an in-intensity best evaluation. This entails defect detection and the assessment of ripeness, making sure that the most effective rate-satisfactory merchandise reaches customers. The implementation of sturdy and scalable systems has adeptly addressed demanding situations associated with facts, model education, and real-time processing. Consequently, the Fresh Food Identification System emerges as a pivotal contributor to raising the standards of the perishable meals supply chain, embodying a dedication to excellence and performance. Keywords— Fresh Fruit Detection, Machine Learning, Computer Vision, Fruit Quality Assessment, Food Safety, Image Processing,Classification Algorithms, Object Detection, Feature Extraction, Deep Learning,Convolutional Neural Networks (CNN).

Recent advances in micro-pixel light emitting diode technology

Display technology has developed rapidly in recent years, with III–V system-based micro-light-emitting diodes (μLEDs) attracting attention as a means to overcome the physical limitations of current display systems related to their lifetime, brightness, contrast ratio, response time, and pixel size. However, for μLED displays to be successfully commercialized, their technical shortcomings need to be addressed. This review comprehensively discusses important issues associated with μLEDs, including the use of the ABC model for interpreting their behavior, size-dependent degradation mechanisms, methods for improving their efficiency, novel epitaxial structures, the development of red μLEDs, advanced transfer techniques for production, and the detection and repair of defects. Finally, industrial efforts to commercialize μLED displays are summarized. This review thus provides important insights into the potential realization of next-generation display systems based on μLEDs.

Tensile testing data of additive manufactured ASTM D638 standard specimens with embedded internal geometrical features

Additive manufacturing (AM) is now widely used for research and industrial production. The benchmark data for mechanical properties of additively manufactured specimens is very useful for many communities. This data article presents a tensile testing dataset of ASTM D638 size specimens without and with embedded internal geometrical features printed using polylactic acid (PLA) in a Fused Filament Fabrication (FFF) additive manufacturing process. The added features can mimic defects of various shapes and sizes. This work is a supplement to the published research article Assisted defect detection by in-process monitoring of additive manufacturing using optical imaging and infrared thermography (Additive Manufacturing, 2023, 103483). The printed specimens were tensile tested. Stress-strain graphs were developed and used to calculate the mechanical properties such as ultimate tensile strength (UTS) and strain at UTS. The mechanical properties, the correlations between mechanical properties and size, shape and location of geometrical features (defects), and the trends in mechanical properties can be useful in benchmarking the results of other researchers.

Maize Seed Defect Detection Using Deep Learning

In the realm of agriculture, ensuring high-quality seeds is pivotal for maximizing crop yield and productivity. This study delves into the application of deep learning techniques, specifically Google Net, to address the challenge of maize seed defect detection. By utilizing convolutional neural networks (CNNs) and leveraging the power of transfer learning, this research aims to accurately identify and classify various defects in maize seeds. The technique begins with dataset curation and preprocessing, followed by the adaptation of the Google Net architecture. The model is then trained, validated, and tested, with performance metrics. Keywords: GoogleNet, Convolutional Neural network, Deep learning, Image processing, Segmentation.

Tiny machine learning empowers climbing inspection robots for real-time multiobject bolt-defect detection

Ensuring the structural integrity of steel construction is critical, necessitating effective methods for detecting bolt defects. Traditional inspection methods are reliable but require significant labor and struggle with the accessibility of complex structures. This research introduces a real-time bolt-defect detection system that integrates tiny machine learning (TinyML) with a magnetic climbing robot to enhance inspection efficiency and scope. The system employs the faster objects, more objects (FOMO) algorithm optimized for edge computing on microcontrollers. This approach enables accurate bolt identification, including normal, loose, and missing bolts. The average system accuracy is 82%, with precision and recall values from 0.57 to 0.89 and 0.67 to 0.87, respectively. The system demonstrates balanced detection, evidenced by an F1 score improvement of up to 62%. The FOMO model displays compelling performance on defect detection tasks, achieving an F1 score of approximately 75%, outperforming the MobileNetV2 Single Shot Multibox Detector with Feature Pyramid Networks Lite and You Only Look Once version 5 small. The efficiency of the FOMO model is highlighted by its low hardware requirements at less than 0.1 MB of flash memory and 893.8 KB of random access memory for the 32-bit floating-point data format and is reduced for the 8-bit integer data format, with inference times of 142 ms and 86 ms, respectively. These findings contrast with the higher resource demands and slower inference time of the compared models, indicating the suitability of the FOMO model for low-capacity microcontrollers and its feasibility for real-time applications. The performance analysis across scenarios confirms the high precision (average 0.77) and recall (average 0.76), validating the robustness of the model in diverse environmental conditions. The system offers an advancement over traditional bolt-defect detection methods in accuracy and efficiency by leveraging TinyML and a magnetic climbing robot, setting new benchmarks for real-time inspection technology. The quantitative results underscore the performance of the system, presenting a scalable, cost-effective solution for improving the safety and durability of steel structures.

Expanding the defect image dataset of composite material coating with enhanced image-to-image translation

In the defect detection based on machine vision, the limited datasets have consistently impeded the efficacy of deep learning in defect identification. Restricted datasets lead to inadequate model generalization and reduced detection accuracy. Creating simulated images is one proposed solution to address this challenge. However, existing defect image generation methods have limitations in diversity and realism. An enhanced Image-to-image translation method, named E-Pix2Pix, is proposed for enhancing defect image datasets of composite material coating on solid motors to expand the current dataset and enhance the precision of classification models. This method not only improves the quality of generated images, but also has strong generalization ability. This study examines how different convolution kernel sizes and the coefficient of the L1 loss function affect the generation performance. The results of the experiments show that the defect images generated by E-Pix2Pix have realistic visual effects and better capture the essential features of defects. The structure similarity index measurement (SSIM) value reaches approximately 0.9 under appropriate parameters. Moreover, this study intends to investigate how the content of generated images impacts the classification model. By doubling the dataset size using the generated images, the accuracy of ResNet50 model recognition can be improved by 7%. The accuracy of ResNet101 model recognition can be improved by 5% when the dataset is expanded to 1.5 times. The generated images have harmful effects on the weaker models. Our method has also been validated on two public datasets, and satisfactory results have been obtained.