Automatic detection of welding defects using the convolutional neural network

Quality control of welded joints is an important step before commissioning of various types of metal structures. The main obstacles to the commissioning of such facilities are the areas where the welded joint deviates from acceptable defective standards. The defects of welded joints include non-welded, foreign inclusions, cracks, pores, etc. The article describes an approach to the detection of the main types of defects of welded joints using a combination of convolutional neural networks and support vector machine methods. Convolutional neural networks are used for primary classification. The support vector machine is used to accurately define defect boundaries. As a preprocessing in our work, we use the methods of morphological filtration. A series of experiments confirms the high efficiency of the proposed method in comparison with pure CNN method for detecting defects

A deep learning approach to crack detection on road surfaces

Currently, modern achievements in the field of deep learning are increasingly being applied in practice. One of the practical uses of deep learning is to detect cracks on the surface of the roadway. The destruction of the roadway is the result of various factors: for example, the use of low-quality material, non-compliance with the standards of laying asphalt, external physical impact, etc. Detection of these damages in automatic mode with high speed and accuracy is an important and complex task. An effective solution to this problem can reduce the time of services that carry out the detection of damage and also increase the safety of road users. The main challenge for automatically detecting such damage, in most cases, is the complex structure of the roadway. To accurately detect this damage, we use U-Net. After that we improve the binary map with localized cracks from the U-Net neural network, using the morphological filtering. This solution allows localizing cracks with higher accuracy in comparison with traditional methods crack detection, as well as modern methods of deep learning. All experiments were performed using the publicly available CRACK500 dataset with examples of cracks and their binary maps

Automated visual inspection algorithm for the reflection detection and removing in image sequences

Specular reflections are undesirable phenomena that can impair overall perception and subsequent image analysis. In this paper, we propose a modern solution to this problem, based on the latest achievements in this field. The proposed method includes three main steps: image enhancement, detection of specular reflections, and reconstruction of damaged areas. To enhance and equalize the brightness characteristics of the image, we use the alpha-rooting method with an adaptive choice of the optimal parameter alpha. To detect specular reflections, we apply morphological filtering in the HSV color space. At the final stage, there is a reconstruction of damaged areas using adversarial neural networks. This combination makes it possible to quickly and effectively detect and remove specular reflections, which is confirmed by a series of experiments given by the experimental section of this work

Image defect detection algorithm based on deep learning

In this paper proposed a system for automatic defects detection in images. The solution to this problem is widely used in practice. Automatic detection is found in the challenge of detecting defects on the road surface, in the textile industry, as well as virtual restoration of archival photo images. The solution to this range of problems allows speeding up work in these areas, and in some cases, completely solving. To solve the first two problems (search for defects on the pavement and textiles), it is enough to create a mask that localizes defects in the image with maximum reliability, while photo restoration requires additional algorithms to restore the detected damaged areas. The proposed method is based on the latest achievements in the field of machine learning and allows solve the main disadvantages of traditional methods. Automatic defect detection is performed using a neural network with compound descriptor. A series of experiments confirmed the high efficiency of the proposed method in comparison with traditional methods for detecting defects

Crack detection in paintings using convolutional neural networks

The accurate detection of cracks in paintings, which generally portray rich and varying content, is a challenging task. Traditional crack detection methods are often lacking on recent acquisitions of paintings as they are poorly adapted to high-resolutions and do not make use of the other imaging modalities often at hand. Furthermore, many paintings portray a complex or cluttered composition, significantly complicating a precise detection of cracks when using only photographic material. In this paper, we propose a fast crack detection algorithm based on deep convolutional neural networks (CNN) that is capable of combining several imaging modalities, such as regular photographs, infrared photography and X-Ray images. Moreover, we propose an efficient solution to improve the CNN-based localization of the actual crack boundaries and extend the CNN architecture such that areas where it makes little sense to run expensive learning models are ignored. This allows us to process large resolution scans of paintings more efficiently. The proposed on-line method is capable of continuously learning from newly acquired visual data, thus further improving classification results as more data becomes available. A case study on multimodal acquisitions of the Ghent Altarpiece, taken during the currently ongoing conservation-restoration treatment, shows improvements over the state-of-the-art in crack detection methods and demonstrates the potential of our proposed method in assisting art conservators

Defect detection on videos using neural network

In this paper, we consider a method for defects detection in a video sequence, which consists of three main steps; frame compensation, preprocessing by a detector, which is base on the ranking of pixel values, and the classification of all pixels having anomalous values using convolutional neural networks. The effectiveness of the proposed method shown in comparison with the known techniques on several frames of the video sequence with damaged in natural conditions. The analysis of the obtained results indicates the high efficiency of the proposed method. The additional use of machine learning as postprocessing significantly reduce the likelihood of false alarm

Treatment of patients with anterior urethral strictures: the role of perineal urethrostomy

The article reviews the 2000-2020 literature on the use of perineal urethrostomy in the treatment of patients with anterior urethral strictures. Historical issues of the development of urethrostomy techniques are considered. The algorithms to choose the method of treatment of urethral strictures in favor of perineal urethrostomy are highlighted, according to the guidelines of the world's professional urological associations. The performance indicators of perineal urethrostomy were studied considering the age characteristics of the patients, the etiological genesis of the strictures, their length and location, as well as depending on the surgical technique and the follow-up period. Considerable attention is paid to studies devoted to the analysis of the functional results of urethrostomy, as well as the quality of life of patients associated with urination and sexual activity. Data are presented on the incidence of early and late surgical complications, including urethrostomy stenosis, as one of the most common. An analysis of studies evaluating factors that negatively affect the outcome of surgery was carried out. The main reasons for the growing demand for the technique in surgery for complex anterior urethral strictures and the importance of the technique among other treatment methods are discussed

Multi-stage urethroplasy for anterior urethral strictures: objective parameters of long-term efficacy and patient-reported outcomes

Introduction. Multi-stage urethral surgery is used in cases of the most complex urethral strictures. The evaluation of surgical treatment results given by patients is a significant criterion for the efficacy of urethroplasty along with the assessment of urethral patency through instrumental examinations.Objective. To evaluate the long-term efficacy of multistage urethroplasty for complex anterior urethral strictures considering the patients' quality of life and satisfaction with the surgical outcomes.Materials and methods. The study included 73 patients aged 18 – 84 years with anterior urethral strictures who underwent multi-stage urethroplasty in 2010 – 2019. Surgical and functional outcomes of urethroplasty were assessed through general blood and urine tests, physical examination, uroflowmetry, and retrograde urethrography and urethroscopy in case of urinary disorders.  Subjective parameters of treatment efficacy were studied using questionnaires: International Prostate Symptom Score (IPSS); Quality of life (QoL); Patient-reported Outcome Measure for Urethral Stricture Surgery (USS-PROM); Patient Global Impression of Improvement (PGI-I).Results. Recurrent urethral stricture was detected in 19 (26,0%) patients with the average follow-up period being 65 months. Independent urination was restored in 71 (97.3%) cases, including repeated interventions. After surgery, there was a significant increase in urinary flow rate parameters (Q max: 8.1 vs 19.1 ml/s, p < 0.0001; Q ave: 5.5 vs 10.7 ml/s; p = 0.0004), decrease in residual urine volume (62.4 vs 18.6 ml, p < 0.0001), decrease in total IPSS score (18.7 vs 5.7 points; p < 0.0001) and QoL index (4.3 vs 1 .8 points, p < 0.0001). A comparative analysis of preoperative and postoperative USS-PROM questionnaire results demonstrated an improvement in indicators assessing LUTS (12.9 vs 3.4 points; p < 0.0001; 3.6 vs 1.7 points; p < 0.0001), and urination-associated quality of life (2.6 vs 0.6 points; p < 0.0001) and overall health (EQ-5D index: 0.73 vs 0.91 points; p = 0.025; EQ-VAS: 68.0 vs 88.1 points, p = 0.004). Fifty-seven (81.4%) men were “very satisfied” or “satisfied” with the treatment outcomes, while nine (12.9%) respondents noted a moderate effect of residual urinary disorders on the quality of life. Significantly higher satisfaction was observed among cystostomy patients and in cases where repeated interventions were unnecessary.Conclusion. Multi-stage urethroplasty for complex anterior urethral strictures achieves efficacy in 97.3% of cases and is accompanied by high levels of quality of life and patient’s satisfaction during long-term follow-up