DEVELOPMENT OF A MACHINE VISION SYSTEM FOR DAMAGE AND OBJECT DETECTION IN TUNNELS USING CONVOLUTIONAL NEURAL NETWORKS

Tunnel inspection, i.e. detection of damages and defects on concrete surfaces, is essential for monitoring structural reliability and health conditions of transport facilities, thus providing safe and sustainable urban transportation infrastructures. In this study, an innovative visual-based system is developed for damage and object detection tasks in roadway tunnels based on deep learning techniques. The main components of the developed Machine Vision System such as industrial cameras, flash-based light sources, controller, the synchronization unit and corresponding software programs are designed to collect high-resolution images with sufficient quality from dimly lit tunnel environments in normal traffic flows with an operating speed of 30–50 km/h. Unlike recent studies, the training data includes multiple types of damage such as cracks, spalling, rust, delamination and other surface changes. Furthermore, 10 classes of common tunnel objects including traffic signs, traffic cameras, traffic lights, ventilation ducts, various sensors and cables are labeled for object detection. As state-of-the-art Convolutional Neural Networks, DeepLab and U-Net are trained and evaluated using accuracy metrics for image segmentation. The results highlight the most important parameters of the discussed Machine Vision System as well as the performance of DeepLab and U-Net for object and damage detection

The importance of small non-coding RNAs in human reproduction: A review article

Background: MicroRNAs (miRNA) play a key role in the regulation of gene expression through the translational suppression and control of post-transcriptional modifications. Aim: Previous studies demonstrated that miRNAs conduct the pathways involved in human reproduction including maintenance of primordial germ cells (PGCs), spermatogenesis, oocyte maturation, folliculogenesis and corpus luteum function. The association of miRNA expression with infertility, polycystic ovary syndrome (PCOS), premature ovarian failure (POF), and repeated implantation failure (RIF) was previously revealed. Furthermore, there are evidences of the importance of miRNAs in embryonic development and implantation. Piwi-interacting RNAs (piRNAs) and miRNAs play an important role in the post-transcriptional regulatory processes of germ cells. Indeed, the investigation of small RNAs including miRNAs and piRNAs increase our understanding of the mechanisms involved in fertility. In this review, the current knowledge of microRNAs in embryogenesis and fertility is discussed. Conclusion: Further research is necessary to provide new insights into the application of small RNAs in the diagnosis and therapeutic approaches to infertility

Automatic Classification of Roof Shapes for Multicopter Emergency Landing Site Selection

Geographic information systems (GIS) now provide accurate maps of terrain, roads, waterways, and building footprints and heights. Aircraft, particularly small unmanned aircraft systems, can exploit additional information such as building roof structure to improve navigation accuracy and safety particularly in urban regions. This paper proposes a method to automatically label building roof shape types. Satellite imagery and LIDAR data from Witten, Germany are fed to convolutional neural networks (CNN) to extract salient feature vectors. Supervised training sets are automatically generated from pre-labeled buildings contained in the OpenStreetMap database. Multiple CNN architectures are trained and tested, with the best performing networks providing a condensed feature set for support vector machine and decision tree classifiers. Satellite and LIDAR data fusion is shown to provide greater classification accuracy than through use of either data type individually

KNOWLEDGE BASED 3D BUILDING MODEL RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS FROM LIDAR AND AERIAL IMAGERIES

In recent years, with the development of the high resolution data acquisition technologies, many different approaches and algorithms have been presented to extract the accurate and timely updated 3D models of buildings as a key element of city structures for numerous applications in urban mapping. In this paper, a novel and model-based approach is proposed for automatic recognition of buildings’ roof models such as flat, gable, hip, and pyramid hip roof models based on deep structures for hierarchical learning of features that are extracted from both LiDAR and aerial ortho-photos. The main steps of this approach include building segmentation, feature extraction and learning, and finally building roof labeling in a supervised pre-trained Convolutional Neural Network (CNN) framework to have an automatic recognition system for various types of buildings over an urban area. In this framework, the height information provides invariant geometric features for convolutional neural network to localize the boundary of each individual roofs. CNN is a kind of feed-forward neural network with the multilayer perceptron concept which consists of a number of convolutional and subsampling layers in an adaptable structure and it is widely used in pattern recognition and object detection application. Since the training dataset is a small library of labeled models for different shapes of roofs, the computation time of learning can be decreased significantly using the pre-trained models. The experimental results highlight the effectiveness of the deep learning approach to detect and extract the pattern of buildings’ roofs automatically considering the complementary nature of height and RGB information

The use of bivariate copulas for bias correction of reanalysis air temperature data: University of Twente

Air temperature data retrieved from global atmospheric models may show a systematic bias with respect to measurements from weather stations. This is a common concern in local climate studies. The current study presents two methods based upon copulas and Conditional Probability (CP) to predict bias-corrected mean air temperature in a data-scarce environment: CP-I offers a single conditional probability as a predictor, CP-II is a pixel-wise version of CP-I and offers spatially varying predictors. The methods were applied on daily air temperature in the Qazvin Plain, Iran that were collected at 24 weather stations and 150 ECMWF ERA-interim grid cells from a single month (June) over 11 years. We compared the methods with the commonly applied conditional expectation and conditional median methods

STATISTICAL EVALUATION OF FITTING ACCURACY OF GLOBAL AND LOCAL DIGITAL ELEVATION MODELS IN IRAN

Digital Elevation Models (DEMs) are one of the most important data for various applications such as hydrological studies, topography mapping and ortho image generation. There are well-known DEMs of the whole world that represent the terrain's surface at variable resolution and they are also freely available for 99% of the globe. However, it is necessary to assess the quality of the global DEMs for the regional scale applications.These models are evaluated by differencing with other reference DEMs or ground control points (GCPs) in order to estimate the quality and accuracy parameters over different land cover types. In this paper, a comparison of ASTER GDEM ver2, SRTM DEM with more than 800 reference GCPs and also with a local elevation model over the area of Iran is presented. This study investigates DEM’s characteristics such as systematic error (bias), vertical accuracy and outliers for DEMs using both the usual (Mean error, Root Mean Square Error, Standard Deviation) and the robust (Median, Normalized Median Absolute Deviation, Sample Quantiles) descriptors. Also, the visual assessment tools are used to illustrate the quality of DEMs, such as normalized histograms and Q-Q plots. The results of the study confirmed that there is a negative elevation bias of approximately 5 meters of GDEM ver2. The measured RMSE and NMAD for elevation differences of GDEM-GCPs are 7.1 m and 3.2 m, respectively, while these values for SRTM and GCPs are 9.0 m and 4.4 m. On the other hand, in comparison with the local DEM, GDEM ver2 exhibits the RMSE of about 6.7 m, a little higher than the RMSE of SRTM (5.1 m).The results of height difference classification and other statistical analysis of GDEM ver2-local DEM and SRTM-local DEM reveal that SRTM is slightly more accurate than GDEM ver2. Accordingly, SRTM has no noticeable bias and shift from Local DEM and they have more consistency to each other, while GDEM ver2 has always a negative bias

THE DESIGN AND IMPLEMENTATION OF AN OPTICAL ASTRONOMICAL SATELLITE TRACKING SYSTEM

This paper presents a satellite tracking method based on optical celestial images. The proposed method is composed of two acquiring modes that are called Sidereal Stare Mode (SSM) and Track Rate Mode (TRM). To track the unknown satellites, first of all the entire of the sky is observed and the primary knowledge of the satellite location is predicted in SSM. Then, the measured pointing data is calculated and used by the telescope to acquire the satellite tracking images at the appropriate time. The framework of SSM contains main steps such as the image correction due to the noise and the image background by applying the iterative filtering methods, the star detection and removal using a double gate filter and PSF concepts, the star identification for image calibration based on a star catalogue, the streak detection of the satellite using the matched filter and finally, the extraction of the celestial coordinate of the satellite as a predicted position. In TRM, the group of star streaks should be identified in the free background image. Then, Fast Fourier Transformation (FFT) is applied and the characteristic of star streaks are extracted. Finally, the centroid of the satellite is estimated precisely by applying a binary mask and the right ascension and declination of it is calculated using astrometric transformation parameters. In this study, the searching and tracking algorithms is applied on the simulation images to detect and identify the satellite position in the sky. Therefore, a telescope along with a CCD camera is simulated to generate sequences images using the well-known star catalogues, the optical and orbital parameters. The precise celestial coordinate of a real satellite and two fictitious satellites are obtained from simulated images. The study results show that the accuracy of the satellite estimated position after star calibration is better than 5 arc seconds and the satellites tracking accuracy is around 1–3 arc seconds

AN IMAGE-BASED TECHNIQUE FOR 3D BUILDING RECONSTRUCTION USING MULTI-VIEW UAV IMAGES

Nowadays, with the development of the urban areas, the automatic reconstruction of the buildings, as an important objects of the city complex structures, became a challenging topic in computer vision and photogrammetric researches. In this paper, the capability of multi-view Unmanned Aerial Vehicles (UAVs) images is examined to provide a 3D model of complex building façades using an efficient image-based modelling workflow. The main steps of this work include: pose estimation, point cloud generation, and 3D modelling. After improving the initial values of interior and exterior parameters at first step, an efficient image matching technique such as Semi Global Matching (SGM) is applied on UAV images and a dense point cloud is generated. Then, a mesh model of points is calculated using Delaunay 2.5D triangulation and refined to obtain an accurate model of building. Finally, a texture is assigned to mesh in order to create a realistic 3D model. The resulting model has provided enough details of building based on visual assessment