CNN-Based Watershed Marker Extraction for Brick Segmentation in Masonry Walls

Nowadays there is an increasing need for using artificial intelligence techniques in image-based documentation and survey in archeology, architecture or civil engineering applications. Brick segmentation is an important initial step in the documentation and analysis of masonry wall images. However, due to the heterogeneous material, size, shape and arrangement of the bricks, it is highly challenging to develop a widely adoptable solution for the problem via conventional geometric and radiometry based approaches. In this paper, we propose a new technique which combines the strength of deep learning for brick seed localization, and the Watershed algorithm for accurate instance segmentation. More specifically, we adopt a U-Net-based delineation algorithm for robust marker generation in the Watershed process, which provides as output the accurate contours of the individual bricks, and also separates them from the mortar regions. For training the network and evaluating our results, we created a new test dataset which consist of 162 hand-labeled images of various wall categories. Quantitative evaluation is provided both at instance and at pixel level, and the results are compared to two reference methods proposed for wall delineation, and to a morphology based brick segmentation approach. The experimental results showed the advantages of the proposed U-Net markered Watershed method, providing average F1-scores above 80%

SISTEMA INFORMÁTICO PARA LA CLASIFICACIÓN AUTOMÁTICA DE IMÁGENES DE GRANOS DE POLEN / COMPUTER SYSTEM FOR THE AUTOMATIC CLASSIFICATION OF POLLEN GRAIN IMAGES

La presente investigación ofrece un sistema que facilita el proceso de clasificación de imágenes de granos de polen de La Empresa Apícola Cubana, cuyos resultados demostraron que existe lentitud en el proceso de análisis polínicos de las mieles, ya que las especies que pertenecen a una misma familia comparten características de identificación. Por esta razón se hizo necesario desarrollar un sistema informático que permitiera a los técnicos melisopalinólogos clasificar automáticamente una imagen de grano de polen. La implementación del sistema se realizó utilizando Keras para la creación de redes neuronales convolucionales y Tensor Flow para el trabajo con imágenes, ambas librerías de Python lo que posibilita su empleo en cualquier plataforma. Para guiar el proceso de desarrollo se utilizó la metodología Rational Unified Process (RUP). El sistema propuesto posibilita el identificación y clasificación rápida de imágenes de granos de polen. Almacena un conjunto de datos que permite al sistema identificar las especies de plantas

An innovative image processing-based framework for the numerical modelling of cracked masonry structures

A vital aspect when modelling the mechanical behaviour of existing masonry structures is the accuracy in which the geometry of the real structure is transferred in the numerical model. Commonly, the geometry of masonry is captured with traditional techniques (e.g. visual inspection and manual surveying methods), which are labour intensive and error-prone. Over the last ten years, advances in photogrammetry and image processing have started to change the building industry since it is possible to capture rapidly and remotely digital records of objects and features. Although limited work exists in detecting distinct features from masonry structures, up to now there is no automated procedure leading from image-based recording to their numerical modelling. To address this, an innovative framework, based on image-processing, has been developed that automatically extracts geometrical features from masonry structures (i.e. masonry units, mortar, existing cracks and pathologies, etc.) and generate the geometry for their advanced numerical modelling. The proposed watershed-based algorithm initially deconstructs the features of the segmentation, then reconstructs them in the form of shared vertices and edges, and finally converts them to scalable polylines. The polylines extracted are simplified using a contour generalisation procedure. The geometry of the masonry elements is further modified to facilitate the transition to a numerical modelling environment. The proposed framework is tested by comparing the numerical analysis results of an undamaged and a damaged masonry structures, using models generated through manual and the proposed algorithmic approaches. Although the methodology is demonstrated here for use in discrete element modelling, it can be applied to other computational approaches based on the simplified and detailed micro-modelling approach for evaluating the structural behaviour of masonry structures

Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning

Masonry structures represent the highest proportion of building stock worldwide. Currently, the structural condition of such structures is predominantly manually inspected which is a laborious, costly and subjective process. With developments in computer vision, there is an opportunity to use digital images to automate the visual inspection process. The aim of this study is to examine deep learning techniques for crack detection on images from masonry walls. A dataset with photos from masonry structures is produced containing complex backgrounds and various crack types and sizes. Different deep learning networks are considered and by leveraging the effect of transfer learning crack detection on masonry surfaces is performed on patch level with 95.3% accuracy and on pixel level with 79.6% F1 score. This is the first implementation of deep learning for pixel-level crack segmentation on masonry surfaces. Codes, data and networks relevant to the herein study are available in: github.com/dimitrisdais/crack_detection_CNN_masonry

Monitoring, modelling and quantification of accumulation of damage on masonry structures due to recursive loads

The use of induced seismicity is gaining in popularity, particularly in Northern Europe, as people strive to increase local energy supplies. Τhe local building stock, comprising mainly of low-rise domestic masonry structures without any aseismic design, has been found susceptible to these induced tremors. Induced seismicity is generally characterized by frequent small-to-medium magnitude earthquakes in which structural and non-structural damage have been reported. Since the induced earthquakes are caused by third parties liability issues arise and a damage claim mechanism is activated. Typically, any damage are evaluated by visual inspections. This damage assessment process has been found rather cumbersome since visual inspections are laborious, slow and expensive while the identification of the cause of any light damage is a challenging task rendering essential the development of a more reliable approach. The aim of this PhD study is to gain a better understanding of the monitoring, modelling and quantification of accumulation of damage in masonry structures due to recursive loads. Fraeylemaborg, the most emblematic monument in the Groningen region dating back to the 14 th century, has experienced damage due to the induced seismic activity in the region in recent years. A novel monitoring approach is proposed to detect damage accumulation due to induced seismicity on the monument. Results of the monitoring, in particular the monitoring of the effects of induced seismic activity,, as well as the usefulness and need of various monitoring data for similar cases are discussed. A numerical model is developed and calibrated based on experimental findings and different loading scenarios are compared with the actual damage patterns observed on the structure. Vision-based techniques are developed for the detection of damage accumulation in masonry structures in an attempt to enhance effectiveness of the inspection process. In particular, an artificial intelligence solution is proposed for the automatic detection of cracks on masonry structures. A dataset with photographs from masonry structures is produced containing complex backgrounds and various crack types and sizes. Moreover, different convolutional neural networks are evaluated on their efficacy to automatically detect cracks. Furthermore, computer vision and photogrammetry methods are considered along with novel invisible markers for monitoring cracks. The proposed method shifts the marker reflection and its contrast with the background into the invisible wavelength of light (i.e. to the near-infrared) so that the markers are not easily distinguishable. The method is thus particularly vi suitable for monitoring historical buildings where it is important to avoid any interventions or disruption to the authenticity of the basic fabric of construction.. Further on, the quantification and modelling of damage in masonry structures are attempted by taking into consideration the initiation and propagation of damage due to earthquake excitations. The evaluation of damage in masonry structures due to (induced) earthquakes represents a challenging task. Cumulative damage due to subsequent ground motions is expected to have an effect on the seismic capacity of a structure. Crack patterns obtained from experimental campaigns from the literature are investigated and their correlation with damage propagation is examined. Discontinuous modelling techniques are able to reliably reproduce damage initiation and propagation by accounting for residual cracks even for low intensity loading. Detailed models based on the Distinct Element Method and Finite Element Model analysis are considered to capture and quantify the cumulative damage in micro level in masonry subjected to seismic loads. Finally, an experimental campaign is undertaken to investigate the accumulation of damage in masonry structure under repetitive load. Six wall specimens resembling the configuration of a spandrel element are tested under three-point in-plane bending considering different loading protocols. The walls were prepared adopting materials and practices followed in the Groningen region. Different numerical approaches are researched for their efficacy to reproduce the experimental response and any limitations are highlighted