Visual Inspection Method Research Articles

Accuracy of Visual Inspection Methods for the Diagnosis of Cervical Precancerous Lesions

Accuracy of Visual Inspection Methods for the Diagnosis of Cervical Precancerous Lesions

Review on quick safety assessment of building structures in complex urban environment after extreme explosion events

Extreme explosion events result in demands for emergency rescue service. From the civil engineering perspective, a quick safety assessment of building structures in the explosion’s vicinity will provide the emergency rescue committee with concrete support to make scientific decisions. In this paper, three primary issues, namely, inverse analysis of explosive characteristics, blast wave propagation in complex urban areas and blast-induced damage identification, are reviewed. These are often performed stepwise and form a multi-step whole to assist the emergency rescue service. The paper begins by introducing the inverse analysis of explosives based on craters, building damages and seismic or acoustic records. In this step, explosive characteristics, for example, charge type, original time, yield and location, could be produced and input into blast load calculation in the next step. Then, the existing literature on blast wave propagation and blast load determination is presented with close attention to complex urban environments. It shows that the current study remains in its infancy and relies on advancement in computational fluid dynamics (CFD). Besides, pressure–impulse (P-I) diagrams which predict the structural damage based on the calculated blast loads are illustrated. Onsite damage detection techniques, such as visual inspection, non-destructive testing (NDT) and vibration-based methods, are also discussed. The paper ends with a discussion of the shortcomings of previous work and the outlooks of further work.

End-of-Life Corrosion Estimation and Profile of Ship Hull Structure: Nonparametric Statistical Analysis of Medium Endurance Cutters

Abstract Corrosion in hull structure of Coast Guard cutters is a primary degradation mode that accounts for a significant portion of depot budgets and the occasional unavailability of ships in general. Corrosion exhibits great variability spatially and temporally. This paper presents, summarizes, and analyzes a one-of-a-kind dataset for end-of-life corrosion estimation and profile of ship hull structure. The dataset was created over several years and on several vessels, and collected by maintenance personnel at several geographic locations. This study analyzes wastage data due to corrosion that were systematically collected in 2007–2008 from twelve 210-foot medium endurance cutters, commissioned in 1964–1969, in the form of thickness measurement using visual inspection and ultrasonic testing methods. A total of 76,091 thickness measurements were analyzed at positions covering the entire hulls. The measured corrosion levels mean is about 0.02–0.04 in. (1 in. = 25.4 mm), i.e., 6–14% of the as-built thicknesses after no more than 43 years of use of these 12 cutters as of 2007; however, the analysis of outliers indicates that the average wastage values can be misleading in predicting extreme corrosion. A method is proposed for estimating the counts and intensity of outliers. Examining geographic locations of the operations of these cutters and corrosion revealed that southern warm water led to appreciably larger corrosion compared to the northern colder waters, at a ratio of about 1.25–1.5.

Pressure vessel-oriented visual inspection method based on deep learning.

The detection of surface parameters of pressure vessel welds guarantees safe operation. To address the problems of low efficiency and poor accuracy of traditional manual inspection methods, a method for welding morphological parameters combined with vision and structured light is proposed in this study. First, a feature point extraction algorithm for weld parameters based on deep convolution was proposed. An accurate extraction method of weld image feature point coordinates was designed based on the combination of the loss function via seam undercut feature recognition and weld feature point extraction network structure. Second, a training data enhancement method based on the third-order non-uniform rational B-spline (NURBS) curve was proposed to reduce the amount of data collection for training. Finally, a pressure vessel measurement device was designed, and the feature point extraction performance of the deep network and common feature point extraction networks, DeepLabCut and HR-net, proposed in this study were compared to analyze the theoretical accuracy of the surface parameter measurement. The results indicated that the theoretical accuracy of the parameter measurements was within 0.065 mm.

Agreement between visual inspection and objective analysis methods: A replication and extension.

Behavior analysts typically rely on visual inspection of single‐case experimental designs to make treatment decisions. However, visual inspection is subjective, which has led to the development of supplemental objective methods such as the conservative dual‐criteria method. To replicate and extend a study conducted by Wolfe et al. (2018) on the topic, we examined agreement between the visual inspection of five raters, the conservative dual‐criteria method, and a machine‐learning algorithm (i.e., the support vector classifier) on 198 AB graphs extracted from clinical data. The results indicated that average agreement between the 3 methods was generally consistent. Mean interrater agreement was 84%, whereas raters agreed with the conservative dual‐criteria method and the support vector classifier on 84% and 85% of graphs, respectively. Our results indicate that both objective methods produce results consistent with visual inspection, which may support their future use.

A visual inspection method for delamination extraction and quantification of carbon fiber reinforced plastic (CFRP)

Inspection the delamination of composite components is essential to ensure the safety of aircraft in service. Vision inspection has great potential for the drilling quality inspection of large composite components. However, stable extraction and accurate quantification of delamination are extremely challenging, due to the characteristics of low contrast, varying morphology, and random distribution of delamination. To address this problem, robust delamination extraction algorithm and integrated evaluation method are proposed. The extraction of delamination is achieved by contour extraction, robust circle fitting, and adaptive threshold segmentation. Based on this, the statistical quality description factor Qd-s is designed to quantify multi-dimensional information of delamination. Finally, the effectiveness of the proposed method is demonstrated. The experimental result shows that the proposed method outperforms traditional methods, which is expected to become a general technique for the inspection of delamination in the manufacturing process of CFRP.

Hybrid-Learning-Based Operational Visual Quality Inspection for Edge-Computing-Enabled IoT System

Deep learning-enhanced Internet of Things (IoT) plays a pivot role in advancing the transformation toward smart manufacturing, and an essential component in many smart manufacturing IoT systems is the quality inspection. However, challenges, such as expensive data labeling, innumerable types of defects, and high costs for iterative optimization, hinder the industrial applicability of previous visual surface quality inspection methods. In this article, we present an edge-computing-enabled IoT system based on an innovative hybrid learning method for visual surface quality inspection using only few labeled data and minimum iterative optimization efforts. Our hybrid learning method first employs a deep neural network to synthesize global representations of real-world industrial images, which are subsequently analyzed via an unsupervised clustering algorithm for anomaly detection. Besides, enhancement strategies, such as fine-tuning and data augmentation, are proposed to improve the robustness against the noisy data set and support low-cost inference in multiple edge devices for manufacturing operation. On a holdout data set collected from real-world factories, our method achieves classification accuracies between 90% and 98%, outperforming the benchmark method by 7%–12%. Moreover, this hybrid learning method demonstrates the effectiveness in detecting new types of surface defects and achieves test recalls between 86% and 97%, outperforming the benchmark method by 11%–34%.

Development of an Area Scan Step Length Measuring System Using a Polynomial Estimate of the Heel Cloud Point

Due to impaired mobility caused by aging, it is very important to employ early detection and monitoring of gait parameters to prevent the inevitable huge amount of medical cost at a later age. For gait training and potential tele-monitoring application outside clinical settings, low-cost yet highly reliable gait analysis systems are needed. This research proposes using a single LiDAR system to perform automatic gait analysis with polynomial fitting. The experimental setup for this study consists of two different walking speeds, fast walk and normal walk, along a 5-m straight line. There were ten test subjects (mean age 28, SD 5.2) who voluntarily participated in the study. We performed polynomial fitting to estimate the step length from the heel projection cloud point laser data as the subject walks forwards and compared the values with the visual inspection method. The results showed that the visual inspection method is accurate up to 6 cm while the polynomial method achieves 8 cm in the worst case (fast walking). With the accuracy difference estimated to be at most 2 cm, the polynomial method provides reliability of heel location estimation as compared with the observational gait analysis. The proposed method in this study presents an improvement accuracy of 4% as opposed to the proposed dual-laser range sensor method that reported 57.87 cm ± 10.48, an error of 10%. Meanwhile, our proposed method reported ±0.0633 m, a 6% error for normal walking.

Two caseworks for one gene: successful species identification from compromised bone materials with the 12S rRNA.

The availability of a reliable molecular assay in species recognition in forensic cases is of paramount importance when visual inspection or morphological methods are not exhaustive, especially from challenging samples. Here, two different caseworks involving bone samples founded during medico-legal outdoor investigations are presented. In order to exclude the human nature of the specimens and to determine the exact species they belong to, we proceeded with the molecular approach trying to generate sequences from the classical mtDNA markers cyt b and COI. However, they both gave critical results. For this reason, a short amplicon of ~ 150bp of the 12S rRNA gene was used as an alternative.This short fragment was sufficient to identify the biological origin of the bone specimens with a high degree of certainty leading to the exclusion of their human nature. This work highlights the utility of the 12S rRNA and underlines the importance of deepen the choice of alternative shorter markers with respect to the classical ones, in order to achieve species identification even from challenging and degraded material in forensic criminal and wildlife caseworks.

Advanced Damage Assessment Method for Bacterial Leaf Blight Disease in Rice by Integrating Remote Sensing Data for Agricultural Insurance

This study aimed to develop a new method for assessing damage to rice plants by pests and diseases using remote sensing data to enable greater efficiency and accuracy for payment of indemnity in the agricultural insurance system of Indonesia, formally operationalized in 2016. The relationships between bacterial leaf blight (BLB) damage ratio in rice crops evaluated by pest observers using the current visual inspection method and the reflectance of each observation band of RapidEye and Sentinel 2, normalized difference vegetation index (NDVI), green NDVI (GNDVI), and red edge multiplied by the green band index (RGI) were studied. The results showed a positive relationship between BLB damage intensity and reflectance of visible wavelength bands, and a particularly strong positive correlation between the red band and BLB damage intensity. The BLB damage intensity can be evaluated based on pixels and paddy parcels. Time series analysis was conducted using Sentinel-2 data acquired during different plant growth periods such as heading, flowering, ripening, maturity, and harvesting. The results showed a strong correlation between the BLB damage intensity and reflectance of the red edge band at the rice heading and flowering stages; the correlation of BLB damage intensity with the reflectance of the visible range became stronger as the rice plant approached the harvesting stage. This study clearly demonstrated that BLB symptoms can be successfully detected and evaluated approximately one or one and a half months before the harvesting period using remote sensing data. We propose that the BLB damage intensity, currently assessed by pest observers through visual inspection methods, can be calculated from satellite data, suggesting that the satellite sensor could play a role similar to the human eye.

Influence of hydrothermal aging on the mechanical performance of foam core sandwich panels subjected to low-velocity impact

Abstract The effect of hydrothermal aging on the impact resistance of foam core sandwich panels is studied in this study. The sandwich panels with glass fiber-reinforced skins and polyurethane foam core were fabricated and then were treated with different hydrothermal aging conditions. The moisture absorption characteristic of the composite skins was evaluated. A modified Fickian formulation was proposed to predict the moisture absorption behavior of composite skins. The low-velocity impact resistance of the aged sandwich panels was determined at three different impact energies. The impact responses including contact force, deflection, and dissipated energy of the sandwich panels with and without hydrothermal aging were analyzed. The macroscopic and microscopic damage morphologies were observed by visual inspection and scanning electron microscope methods, respectively. The damage mechanism of the aged panels was revealed. Results indicate that the impact resistance of aged sandwich panels is degraded, and the performance degradation is larger with increasing aging temperature. Compared to the panel without hydrothermal aging, the reduction of the contact force is 35.69%, and the increase of the deflection is 71.43% for the aged panel at 70°C aging temperature. The fiber/matrix interfacial cohesive performance is degraded resulting from the hydrothermal aging.

A Contactless PCBA Defect Detection Method: Convolutional Neural Networks With Thermographic Images

In the mass production of electronic products, in-circuit-test (ICT) and printed circuit board assembly (PCBA) quality tests are performed. ICT measures resistance values and capacitance, but not only does it require the use of a fixture that is expensive and requires frequent replacement but also the fixture’s needles may cause PCBA defects. To overcome these limitations, various studies tried to replace ICT using visual inspection methods; however, visual inspection methods cannot be applied to chip resistors and chip capacitors that do not have externally visible characteristics. In this article, we propose a contactless inspection method that can detect PCBA defects without the use of the fixture and ICT by using the comparison of thermal images and deep learning (DL) analysis. We review the existing contactless inspection methods and compare them with our proposed thermal image analysis method. We analyzed thermal images by applying a structural similarity index map as a rule-based object detection method, and we used convolutional neural networks (CNNs), regions with CNN features, and an autoencoder as DL analysis methods. As a result, we achieved highly accurate defective component detection and location in real time.

Evaluation of Gonad Reproductive Condition Using Non-invasive Ultrasonography in Red Abalone (Haliotis rufescens)

The reproductive status in abalone (Haliotis spp.) can be difficult to discern using traditional visual inspection methods and may therefore rely on lethal histological analyses for a definitive determination. With endangered species of abalone, lethal histological examinations are strictly prohibited due to their imperiled status. This research investigated the effectiveness of using non-invasive ultrasound technology as an alternate means of identifying the reproductive status of abalone rather than through invasive biopsies or lethal histological methods. Twelve abalone were randomly selected from cultured red abalone broodstock cohort (n = 200) produced at The Cultured Abalone Farm (Goleta, CA) to initially test these methods. Following initial gonad ultrasound determination, a subset of another cohort of 5 year-old (n = 62) red abalone produced at the UC Davis Bodega Marine Laboratory were monitored using ultrasonography to detect seasonal changes in gonad size for 7 weeks. Following the 7-week assessment period, the full cohort of 5-year old abalone (n = 122) were then spawned and gonads were also evaluated using ultrasound technology before and immediately after spawning. Ultrasound imaging technology accurately differentiated between digestive and reproductive tissues, allowing for the characterization of an ultrasound gonad index score for rapid assessment. Mean gonad thickness determined via ultrasound imaging was categorized on a scale of 1–5, with an index of 1 being the lowest (gonad tissue thin or absent) and an index of 5 being the highest (gonad margin has increased and is compressing the digestive gland) for red abalone. Ultrasound imaging technology was successful in rendering a gonad index score that can be used to track gonad maturation over time in cultured or captive species. Tracking ultrasound gonad scores over time is a useful tool for improving abalone culture production, and increasing animal welfare by reducing handling stress associated with gonad assessments. Individual abalone that are empirically determined ready to be spawned can then be selected for broodstock. Non-invasive ultrasound technology has the potential for broad applications within abalone aquaculture to enhance both food and conservation aquaculture breeding programs.

Non-Destructive Techniques for the Condition and Structural Health Monitoring of Wind Turbines: A Literature Review of the Last 20 Years.

A complete surveillance strategy for wind turbines requires both the condition monitoring (CM) of their mechanical components and the structural health monitoring (SHM) of their load-bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil and mechanical engineering fields. Several traditional and advanced non-destructive techniques (NDTs) have been proposed for both areas of application throughout the last years. These include visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT), radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods. These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs); they can also be applied both for isolated wind turbines or systematically for whole onshore or offshore wind farms. These non-destructive approaches have been extensively reviewed here; more than 300 scientific articles, technical reports, and other documents are included in this review, encompassing all the main aspects of these survey strategies. Particular attention was dedicated to the latest developments in the last two decades (2000–2021). Highly influential research works, which received major attention from the scientific community, are highlighted and commented upon. Furthermore, for each strategy, a selection of relevant applications is reported by way of example, including newer and less developed strategies as well.

Impingement in Insertional Achilles Tendinopathy Occurs Across a Larger Range of Ankle Angles and Is Associated With Increased Tendon Thickness.

Insertional Achilles tendinopathy (IAT) is characterized by tendon degeneration and thickening near the tendon-bone insertion.11 Calcaneal impingement is believed to contribute to the pathogenesis of IAT.5 However, it is unclear how increased tendon thickness in individuals with IAT influences impingement. This study aimed to compare Achilles tendon impingement in individuals with and without IAT. Eight healthy adults and 12 adults with clinically diagnosed symptomatic IAT performed a passive flexion exercise during which ankle flexion angle, anterior-posterior (A-P) thickness, and an ultrasonographic image sequence of the Achilles tendon insertion were acquired. The angle of ankle plantarflexion at which the calcaneus first impinges the Achilles tendon, defined as the impingement onset angle, was identified by (1) a anonymized observer (visual inspection method) and (2) a computational image deformation-based approach (curvature method). Although the 2 methods provided different impingement onset angles, the measurements were strongly correlated (R2 = 0.751, P < .05). The impingement onset angle and the thickness of the Achilles tendon insertion were greater in subjects with clinically diagnosed IAT (P = .0048, P = .0047). Furthermore, impingement onset angle proved to have a moderate correlation with anterior-posterior thickness (R2 = 0.454, P < .05). Our findings demonstrated that increased tendon thickness in IAT patients is associated with larger impingement onset angles, raising the range of ankle angles over which the tendon is exposed to impingement. Increased susceptibility to impingement may exacerbate or perpetuate the pathology, highlighting the need for clinical strategies to reduce impingement in IAT patients.

Oligoclonal Band Straightening Based on Optimized Hierarchical Warping for Multiple Sclerosis Diagnosis.

The detection of immunoglobulin G (IgG) oligoclonal bands (OCB) in cerebrospinal fluid (CSF) by isoelectric focusing (IEF) is a valuable tool for the diagnosis of multiple sclerosis. Over the last decade, the results of our clinical research have suggested that tears are a non-invasive alternative to CSF. However, since tear samples have a lower IgG concentration than CSF, a sensitive OCB detection is therefore required. We are developing the first automatic tool for IEF analysis, with a view to speeding up the current visual inspection method, removing user variability, reducing misinterpretation, and facilitating OCB quantification and follow-up studies. The removal of band distortion is a key image enhancement step in increasing the reliability of automatic OCB detection. Here, we describe a novel, fully automatic band-straightening algorithm. The algorithm is based on a correlation directional warping function, estimated using an energy minimization procedure. The approach was optimized via an innovative coupling of a hierarchy of image resolutions to a hierarchy of transformation, in which band misalignment is corrected at successively finer scales. The algorithm’s performance was assessed in terms of the bands’ standard deviation before and after straightening, using a synthetic dataset and a set of 200 lanes of CSF, tear, serum and control samples on which experts had manually delineated the bands. The number of distorted bands was divided by almost 16 for the synthetic lanes and by 7 for the test dataset of real lanes. This method can be applied effectively to different sample types. It can realign minimal contrast bands and is robust for non-uniform deformations.

Unsupervised Machine Learning for Missing Clamp Detection from an In-Service Train Using Differential Eddy Current Sensor

The rail fastening system plays a crucial role in railway tracks as it ensures operational safety by fixing the rail on to the sleeper. Early detection of rail fastener system defects is crucial to ensure track safety and to enable maintenance optimization. Fastener inspections are normally conducted either manually by trained maintenance personnel or by using automated 2-D visual inspection methods. Such methods have drawbacks when visibility is limited, and they are also found to be expensive in terms of system maintenance cost and track possession time. In a previous study, the authors proposed a train-based differential eddy current sensor system based on the principle of electromagnetic induction for fastener inspection that could overcome the challenges mentioned above. The detection in the previous study was carried out with the aid of a supervised machine learning algorithm. This study reports the finding of a case study, along a heavy haul line in the north of Sweden, using the same eddy current sensor system mounted on an in-service freight train. In this study, unsupervised machine learning models for detecting and analyzing missing clamps in a fastener system were developed. The differential eddy current measurement system was set to use a driving field frequency of 27 kHz. An anomaly detection model combining isolation forest (IF) and connectivity-based outlier factor (COF) was implemented to detect anomalies from fastener inspection measurements. To group the anomalies into meaningful clusters and to detect missing clamps within the fastening system, an unsupervised clustering based on the DBSCAN algorithm was also implemented. The models were verified by measuring a section of the track for which the track conditions were known. The proposed anomaly detection model had a detection accuracy of 96.79% and also exhibited a high score of sensitivity and specificity. The DBSCAN model was successful in clustering missing clamps, both one and two missing clamps, from a fastening system separately.

Surface defect detection of vehicle light guide plates based on an improved RetinaNet

Aiming at the vehicle navigation light guide plate (LGP) image characteristics, such as complex and gradient textures, uneven brightness, and small defects, this paper proposes a visual inspection method for LGP defects based on an improved RetinaNet. First, we use ResNeXt50 with higher accuracy under the same parameters as the backbone network, and propose the lightweight module Ghost_module to replace the 1 × 1 convolution in the lower half of the ResNeXt_block. This can reduce the resource parameters and consumption, and speed up training and inference. Second, we propose and use an improved feature pyramid network module to improve the feature fusion network in RetinaNet. It can more effectively fuse the shallow semantic information and high-level semantic information in the backbone feature extraction network, and further improve the detection ability of small target defects. Finally, the defect detection dataset constructed based on the vehicle LGP images collected at a industrial site, and experiments are performed on the vehicle LGP dataset and Aluminum Profile Defect Identification dataset (Aluminum Profile DID). The experimental results show that the proposed method is both efficient and effective. It achieves a better average detection rate of 98.6% on the vehicle LGP dataset. The accuracy and real-time performance can meet the requirements of industrial detection.

VizSnippets: Compressing Visualization Bundles Into Representative Previews for Browsing Visualization Collections.

Visualization collections, accessed by platforms such as Tableau Online or Power Bl, are used by millions of people to share and access diverse analytical knowledge in the form of interactive visualization bundles. Result snippets, compact previews of these bundles, are presented to users to help them identify relevant content when browsing collections. Our engagement with Tableau product teams and review of existing snippet designs on five platforms showed us that current practices fail to help people judge the relevance of bundles because they include only the title and one image. Users frequently need to undertake the time-consuming endeavour of opening a bundle within its visualization system to examine its many views and dashboards. In response, we contribute the first systematic approach to visualization snippet design. We propose a framework for snippet design that addresses eight key challenges that we identify. We present a computational pipeline to compress the visual and textual content of bundles into representative previews that is adaptive to a provided pixel budget and provides high information density with multiple images and carefully chosen keywords. We also reflect on the method of visual inspection through random sampling to gain confidence in model and parameter choices.

Acceptability, usability and efficacy of an automatic respiratory monitor in determining fast breathing: A pilot study in a tribal tea garden in India.

Childhood pneumonia is one of the leading causes of mortality among under-five children. It is responsible for 15% of all deaths of children U5, killing 808,694 children in 2017 (1). Traditional visual inspection and manual count method is used to detect and classify fast-breathing, a key indicator of Pneumonia. In response to UNICEF's call for a reliable diagnostic tool, Philips was the first to respond with the Children's Automatic Respiratory Monitor for measuring fast breathing objectively. UNICEF and Philips Foundation initiated a field study to test the acceptability, usability and efficacy of the Automatic Respiratory Monitor in Determining Fast Breathing in low resource setting environments. Philips Foundation partnered up with the Directorate of Medical Education in West Bengal, India to conduct the field study amongst community healthcare workers and beneficiaries in a rural district of West Bengal. In collaboration with North Bengal Medical College & Hospital, a community-based study was conducted in a tribal tea garden of Naxalbari block. Acceptability and usability of the device was assessed through structtured interviews and dialogues with community health workers (CHWs), caregivers and local healthcare practitioners. Efficacy of the device was represented by the inter-rater agreement between the traditional visual inspection and manual count method and the device reading. A descriptive community based mixed method study was conducted. Satisfaction among community healthcare workers (CHWs) and beneficiaries was found to be promising across all study parameters. The paper captures the study methods, statistical analysis of the data, the conclusions, areas of further research and recommends community-wide use of the device in objectively measuring fast breathing among children under the age of five years.