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Little is known regarding the usefulness of the smartphone in the detection of uterine cervical lesions or uterine cervical cancer. Therefore, we evaluated the usefulness of the smartphone in the detection of uterine cervical lesions and measured its diagnostic accuracy by comparing its findings with histological findings. We conducted a systematic review to identify studies on the usefulness of the smartphone in detecting uterine cervical lesions indexed in SCOPUS, MEDLINE/PubMed, Cochrane, OVID, Web of Science, and SciELO until November 2020. The risk of bias and applicability was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A total of 16 studies that evaluated the usefulness of the smartphone in the detection of uterine cervical lesions based on the images clicked after visual inspection with acetic acid (VIA), Lugol’s iodine (VILI), or VIA/VILI combination were included in the study. Five studies estimated diagnostic sensitivity and specificity, nine described diagnostic concordance, and five described the usefulness of mobile technology. Among the five first studies, the sensitivity ranged between 66.7% (95% confidence interval (CI); 30.0–90.3%) and 94.1% (95% CI; 81.6–98.3%), and the specificity ranged between 24.0% (95% CI; 9.0–45.0%) and 85.7% (95% CI; 76.7–91.6%). The risk of bias was low (20%), and the applicability was high. In conclusion, the smartphone images clicked after a VIA were found to be more sensitive than those following the VILI method or the VIA/VILI combination and naked-eye techniques in detecting uterine cervical lesions. Thus, a smartphone may be useful in the detection of uterine cervical lesions; however, its sensitivity and specificity are still limited.

This article discusses developing a methodology based on visual inspection for quantifying bridge damage (WABIM). The proposed methodology was developed through the application of weighted averages and a case study. Many current visual inspection methodologies, manuals, or guides related to bridges only allow qualitative results to be determined. Consequently, a high degree of inefficiency and inaccuracy was identified in the results from traditional methodologies; since they have a subjective approach, the results merely depend on the observer. Therefore, a methodological proposal was generated that allowed qualitative results to be described quantitatively, increasing the objectivity of the analysis and the accuracy of bridge maintenance plans. Rating ranges are used with weighted averages for each pathology, applied directly to the structural elements of the bridges. The classification guidelines and pathologies of bridge structures are adapted according to the Manual for the Visual Inspection of Bridges and Pontoons of Invías, Colombia. The case study was developed on a bridge in the city of Pereira, Colombia, presenting more significant surface deterioration and equipment deterioration. The WABIM methodology identified that periodic maintenance is required and the intervention's emphasis.

Manual surface inspection methods performed by quality inspectors do not satisfy the continuously increasing quality standards of industrial manufacturing processes. Machine vision provides a solution by using an automated visual inspection (AVI) system to perform quality inspection and remove defective products. Numerous studies and works have been conducted on surface inspection algorithms. With the advent of deep learning, a number of new algorithms have been developed for better inspection. In this paper, the state-of-the-art in surface defect inspection using deep learning is presented. In particular, we focus on the inspection of industrial products in semiconductor, steel, and fabric manufacturing processes. This work makes three contributions. First, we present the prior literature reviews on vision-based surface defect inspection and analyze the recent AVI-related hardware and software. Second, we review traditional surface defect inspection algorithms including statistical methods, spectral methods, model-based methods, and learning-based methods. Third, we investigate recent advances in deep learning-based inspection algorithms and present their applications in the steel, fabric, and semiconductor industries. Furthermore, we provide information on publicly available datasets containing surface image samples to facilitate the research on deep learning-based surface inspection.

Visual appearance is an important quality factor of pharmaceutical tablets. Moreover, it plays a key role in identification of tablets, which is needed to prevent mix-ups among various types of tablets. Since identification of tablets is most frequently done by imprints, good imprint quality, a property that makes the imprint readable, is of utmost importance in preventing mix-ups among the tablets. In this paper, we propose a novel method for automated visual inspection of tablets. Besides defect detection, imprint quality inspection is also considered. Performance of the method was evaluated on three different real tablet image databases of imprinted tablets. A “gold standard” was established by manually classifying tablets into a good and a defective class. The receiver operating characteristics (ROC) analysis indicated that the proposed method yields better sensitivity and specificity than the previous defect detection method.

This paper addresses a challenging problem of visual inspection of transparent pharmaceutical capsules, where print registration is used to determine the capsule’s print region. The determination of the print region allows for reliable detection of defects both on the print region and on the rest of the capsule’s surface. On transparent capsules, both the print on the front and the print on the back of a capsule are concurrently visible. Moreover, the print on the back may be partially or entirely occluded by the powder inside the capsule. All this causes that the print registration methods used for opaque capsules do not achieve adequate performance. In this paper, we present a novel registration method designed specifically for transparent capsules. The method utilizes a template matching technique with a new similarity measure that considers the specific properties of transparent capsules to increase the registration robustness. Additionally, we present a registration refinement step that reduces the effect of possible print deformations and image distortions. The performance of the method was evaluated in terms of robustness, accuracy and speed on large image sets of four different radial prints. The new method shows highly improved robustness (>98.6 %) compared to the method based on normalized cross-correlation (>72 %) and the method based on feature matching (>80 %). Furthermore, the additional refinement step improves the registration accuracy. Although the execution time is raised from 3 to 11 ms, it still meets the usual speed requirements.

Automated surface-anomaly detection using machine learning has become an interesting and promising area of research, with a very high and direct impact on the application domain of visual inspection. Deep-learning methods have become the most suitable approaches for this task. They allow the inspection system to learn to detect the surface anomaly by simply showing it a number of exemplar images. This paper presents a segmentation-based deep-learning architecture that is designed for the detection and segmentation of surface anomalies and is demonstrated on a specific domain of surface-crack detection. The design of the architecture enables the model to be trained using a small number of samples, which is an important requirement for practical applications. The proposed model is compared with the related deep-learning methods, including the state-of-the-art commercial software, showing that the proposed approach outperforms the related methods on the specific domain of surface-crack detection. The large number of experiments also shed light on the required precision of the annotation, the number of required training samples and on the required computational cost. Experiments are performed on a newly created dataset based on a real-world quality control case and demonstrates that the proposed approach is able to learn on a small number of defected surfaces, using only approximately 25–30 defective training samples, instead of hundreds or thousands, which is usually the case in deep-learning applications. This makes the deep-learning method practical for use in industry where the number of available defective samples is limited. The dataset is also made publicly available to encourage the development and evaluation of new methods for surface-defect detection.

This research establishes a reliability-based preliminary evaluation table for assessing river-bridge flood resistance. Flood resistance for a river-bridge is affected by numerous factors, including bridge structure, river environment, hydrology, and riverbank protection infrastructure. Flood resistance assessment is a complex issue that involves multiple areas of expertise. A comprehensive assessment process is extremely time-consuming and difficult to implement in practice, especially given the limited time and resources. Many bridges require risk evaluations. A preliminary visual inspection is often conducted in response to these problems. The primary issue with visual inspection is the high subjectivity regarding the understanding and standards for the various indicators. To solve this issue, a Bayesian Network (BN) is proposed to combine the contributions from experts and reliability analyses. Eight bridges are selected for performing FOSM-based reliability calculations using a parameterized ABAQUS model. An ideal preliminary inspection table enables a close relationship with the failure probability that is calculated from an advance analysis. Thus, PSO is employed to maximize the correlation between the scores obtained from the visual inspection table and the failure probability calculated from the BN to establish a reliability-based visual inspection table that provides a strong foundation for a bridge risk analysis.

Automobile surface defects like scratches or dents occur during the process of manufacturing and cross-border transportation. This will affect consumers’ first impression and the service life of the car itself. In most worldwide automobile industries, the inspection process is mainly performed by human vision, which is unstable and insufficient. The combination of artificial intelligence and the automobile industry shows promise nowadays. However, it is a challenge to inspect such defects in a computer system because of imbalanced illumination, specular highlight reflection, various reflection modes and limited defect features. This paper presents the design and implementation of a novel automatic inspection system (AIS) for automobile surface defects which are the located in or close to style lines, edges and handles. The system consists of image acquisition and image processing devices, operating in a closed environment and noncontact way with four LED light sources. Specifically, we use five plane-array Charge Coupled Device (CCD) cameras to collect images of the five sides of the automobile synchronously. Then the AIS extracts candidate defect regions from the vehicle body image by a multi-scale Hessian matrix fusion method. Finally, candidate defect regions are classified into pseudo-defects, dents and scratches by feature extraction (shape, size, statistics and divergence features) and a support vector machine algorithm. Experimental results demonstrate that automatic inspection system can effectively reduce false detection of pseudo-defects produced by image noise and achieve accuracies of 95.6% in dent defects and 97.1% in scratch defects, which is suitable for customs inspection of imported vehicles.

Senegal ranks 15th in the world in incidence of cervical cancer, the number one cause of cancer mortality among women in this country. The estimated participation rate for cervical cancer screening throughout Senegal is very low (6.9% of women 18-69 years old), especially in rural areas and among older age groups (only 1.9% of women above the age of 40 years). There are no reliable estimates of the prevalence of cervical dysplasia or risk factors for cervical dysplasia specific to rural Senegal. The goals of this study were to estimate the prevalence of cervical dysplasia in a rural region using visual inspection of the cervix with acetic acid (VIA) and to assess risk factors for cervical cancer control. We conducted a cross-sectional study in which we randomly selected 38 villages across the Kédougou region using a three-stage clustering process. Between October 2013 and March 2014, we collected VIA screening results for women aged 30-50 years and cervical cancer risk factors linked to the screening result. We screened 509 women; 5.6% of the estimated target population (9,041) in the region. The point prevalence of cervical dysplasia (positive VIA test) was 2.10% (95% confidence interval [CI]: 0.99-3.21). Moreover, 287 women completed the cervical cancer risk factor survey (56.4% response rate) and only 38% stated awareness of cervical cancer; 75.9% of the screened women were less than 40 years of age. The overall prevalence of dysplasia in this sample was lower than anticipated. Despite both overall awareness and screening uptake being less than expected, our study highlights the need to address challenges in future prevalence estimates. Principally, we identified that the highest-risk women are the ones least likely to seek screening services, thus illustrating a need to fully understand demand-side barriers to accessing health services in this population. Targeted efforts to educate and motivate older women to seek screenings are needed to sustain an effective cervical cancer screening program.

In today’s era, monitoring the health of the manufacturing environment has become essential in order to prevent unforeseen repairs, shutdowns, and to be able to detect defective products that could incur big losses. Data-driven techniques and advancements in sensor technology with Internet of the Things (IoT) have made real-time tracking of systems a reality. The health of a product can also be continuously assessed throughout the manufacturing lifecycle by using Quality Control (QC) measures. Quality inspection is one of the critical processes in which the product is evaluated and deemed acceptable or rejected. The visual inspection or final inspection process involves a human operator sensorily examining the product to ascertain its status. However, there are several factors that impact the visual inspection process resulting in an overall inspection accuracy of around 80% in the industry. With the goal of 100% inspection in advanced manufacturing systems, manual visual inspection is both time-consuming and costly. Computer Vision (CV) based algorithms have helped in automating parts of the visual inspection process, but there are still unaddressed challenges. This paper presents an Artificial Intelligence (AI) based approach to the visual inspection process by using Deep Learning (DL). The approach includes a custom Convolutional Neural Network (CNN) for inspection and a computer application that can be deployed on the shop floor to make the inspection process user-friendly. The inspection accuracy for the proposed model is 99.86% on image data of casting products.