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

Общественное мнение о справедливости наказания в современном обществе

In digital society, the role of concept of justice is increasing. Various decisions of state authorities are assessed from the standpoint of justice or injustice. However, the concept of justice has special significance in relation to state repression in the context of digitalization. Often society reacts to the use of state coercion against individuals and such situations are widely covered in digital media and the Internet, spark a great public outcry, sometimes lead to various kinds of conflicts between a part of society and officials. It is generally accepted that such situations of social tension are caused by the facts of excessive use of repression. However, we put forward the hypothesis that the perception of justice by digital society and concept of justice set out in the criminal law and perceived by the court are significantly different today, which causes systemic problems in the perception of justice. The solution to this problem is possible only with an integrated approach related to the study of the current criminal law and the potential of justice embedded in it, the perception of justice as a category in the activities of the court, as well as the idea of justice in public perception. The author had the following tasks: 1) to study the main approaches to justice in the modern system of social sciences, 2) set the parameters and forms of polling the population on the justice of punishment, 3) develop an anonymous questionnaire for judges in order to establish the factors, criteria and circumstances which they associate punishment tightening and mitigation with, 4) send the developed questionnaire to all courts of the constituent entities of the Russian Federation, 5) after the responses are received from the courts, carry out selective analysis of the sentences awarded by these courts and compare the circumstances noted in the sentences and affecting the punishment with those indicated by the judges in the questionnaires; 6) process all received sociological data and create the following scales: a) circumstances that should be regarded when assigning a just punishment based on public opinion; b) circumstances that judges regard when choosing a punishment in specific criminal cases. The article presents some results of the study conducted on the basis of a questionnaire survey of judges and the population, as well as a description of the survey methodologyВ цифровом обществе возрастает роль понятия справедливости. Различные решения органов государственной власти оцениваются с позиций справедливости или несправедливости. Однако понятие справедливости имеет особое значение в отношении государственных репрессий в условиях цифровизации. Часто общество реагирует на применение государственного принуждения к личности, и такие ситуации широко освещаются в цифровых СМИ и Интернете, вызывают большой общественный резонанс, иногда приводят к разного рода конфликтам между частью общества и чиновниками. Принято считать, что такие ситуации социальной напряженности вызваны фактами чрезмерного применения репрессий. Однако мы выдвигаем гипотезу о том, что восприятие справедливости цифровым обществом и понятия справедливости, изложенные в уголовном праве и воспринимаемые судом, сегодня существенно различаются, что вызывает системные проблемы в восприятии справедливости. Решение данной проблемы возможно только при комплексном подходе, связанном с изучением действующего уголовного законодательства и заложенных в нем возможностей правосудия, восприятием справедливости как категории в деятельности суда, а также представлением о справедливости в общественном сознании. Перед автором стояли следующие задачи: 1) изучить основные подходы к справедливости в современной системе общественных наук, 2) установить параметры и формы опроса населения о справедливости наказания, 3) разработать анонимную анкету для судей в целях установления факторов, критериев и обстоятельств, с которыми они связывают ужесточение и смягчение наказания, 4) разослать разработанную анкету во все суды субъектов Российской Федерации, 5) после получения ответов судов провести выборочный анализ приговоров, вынесенных этими судами, и сопоставить обстоятельства, отмеченные в приговорах и влияющие на наказание, с указанными судьями в анкетах; 6) обработать все полученные социологические данные и составить следующие шкалы: а) обстоятельства, которые следует учитывать при назначении справедливого наказания на основе общественного мнения; б) обстоятельства, которые судьи учитывают при избрании наказания по конкретным уголовным делам. В статье представлены некоторые результаты исследования, проведенного на основе анкетного опроса судей и населения, а также описание методики опрос

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

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

Role of sociological methodology in determining criteria evaluating justice of punishment in criminal law

In modern society, the role of concept of justice is increasing. Various decisions of state authorities are assessed from the standpoint of justice or injustice. However, the concept of justice has special significance in relation to state repression. Often society reacts to the use of state coercion against individuals and such situations are widely covered in the media, spark a great public outcry, sometimes lead to various kinds of conflicts between a part of society and officials. It is generally accepted that such situations of social tension are caused by the facts of excessive use of repression. However, we put forward the hypothesis that the perception of justice by society and concept of justice set out in the criminal law and perceived by the court are significantly different today, which causes systemic problems in the perception of justice. The solution to this problem is possible only with an integrated approach related to the study of the current criminal law and the potential of justice embedded in it, the perception of justice as a category in the activities of the court, as well as the idea of justice in public perception. The author had the following tasks: 1) to study the main approaches to justice in the modern system of social sciences, 2) set the parameters and forms of polling the population on the justice of punishment, 3) develop an anonymous questionnaire for judges in order to establish the factors, criteria and circumstances which they associate punishment tightening and mitigation with, 4) send the developed questionnaire to all courts of the constituent entities of the Russian Federation, 5) after the responses are received from the courts, carry out selective analysis of the sentences awarded by these courts and compare the circumstances noted in the sentences and affecting the punishment with those indicated by the judges in the questionnaires; 6) process all received sociological data and create the following scales: a) circumstances that should be regarded when assigning a just punishment based on public opinion; b) circumstances that judges regard when choosing a punishment in specific criminal cases. The article presents some results of the study conducted on the basis of a questionnaire survey of judges and the population, as well as a description of the survey methodology

Modified Local and Global Contrast Enhancement Algorithm for Color Satellite Image

The quality of remotely sensed satellite images depends on the reflected electromagnetic radiation from the earth’s surface features. Lack of consistent and similar amounts of energy reflected by different features from the earth’s surface results in a poor contrast satellite image. Image enhancement is the image processing of improving the quality that the results are more suitable for display or further image analysis. In this paper, we present a detailed model for color image enhancement using the quaternion framework. We introduce a novel quaternionic frequency enhancement algorithm that can combine the color channels and the local and global image processing. The basic idea is to apply the α-rooting image enhancement approach for different image blocks. For this purpose, we split image in moving windows on disjoint blocks. The parameter alfa for every block and the weights for every local and global enhanced image driven through optimization of measure of enhancement (EMEC). Some presented experimental results illustrate the performance of the proposed approach on color satellite images in comparison with the state-of-the-art methods

The solution of the problem of simplifying the images for the subsequent minimization of the image bit depth

In this paper, the approach of changing bit depth of images is considered. This type of operation is required when performing primary processing operations, identifying parameters and stitching images. The process of changing bits depth of images is performed in three stages. At each stage, the error minimization criterion is tested Result of applying the approach allows obtaining numerical region characteristics including the number of clusters, the number of minimum and maximum cluster sizes. To perform the process of minimizing some of the criteria, it is necessary to divide the image into areas. The paper presents a mathematical description of the approach, as well as flowcharts for performing operations of data processing steps. The article gives recommendations for choosing coefficients to obtain optimal minimizing parameters. The test images give an example of performing bit changes on image areas

Television images identification in the vision system basis on the mathematical apparatus of cubic normalized B-splines

The solution the task of television image identification is used in industry when creating autonomous robots and systems of technical vision. A similar problem also arises in the development of image analysis systems to function under the influence of various interfering factors in complex observation conditions complicated the registration process and existing when priori information is absent, in background noise type. One of the most important operators is the contour selection operator. Methods and algorithms of processing information from image sensors must take into account the different character of noise associated with images and signals registration. The solution of the task of isolating contours, and in fact of digital differentiation of two-dimensional signals registered against a different character of background noise, is far from trivial. This is due to the fact that such task is incorrect. In modern information systems, methods of numerical differentiation or masks are usually used to solve the task of isolating contours. The paper considers a new method of differentiating measurement results against a noise background using the modern mathematical apparatus of cubic smoothing B-splines. The new high-precision method of digital differentiation of signals using splines is proposed for the first time, without using standard numerical differentiation procedures, to calculate the values of the derivatives with high accuracy. In fact, a method has been developed for calculating the image gradient module using spline differentiation. The method, as proved by experimental studies, and computational experiments has higher noise immunity than algorithms based on standard differentiation procedures using masks